improving resource efficiency of deep activity recognition

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This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Powered by TCPDF (www.tcpdf.org) This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Souza Leite, Clayton; Xiao, Yu Improving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction Published in: HotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications DOI: 10.1145/3376897.3377859 Published: 03/03/2020 Document Version Peer reviewed version Please cite the original version: Souza Leite, C., & Xiao, Y. (2020). Improving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction. In HotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications (pp. 33-38). ACM. https://doi.org/10.1145/3376897.3377859

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Page 1: Improving Resource Efficiency of Deep Activity Recognition

This is an electronic reprint of the original article.This reprint may differ from the original in pagination and typographic detail.

Powered by TCPDF (www.tcpdf.org)

This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user.

Souza Leite, Clayton; Xiao, YuImproving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction

Published in:HotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systems andApplications

DOI:10.1145/3376897.3377859

Published: 03/03/2020

Document VersionPeer reviewed version

Please cite the original version:Souza Leite, C., & Xiao, Y. (2020). Improving Resource Efficiency of Deep Activity Recognition via RedundancyReduction. In HotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systemsand Applications (pp. 33-38). ACM. https://doi.org/10.1145/3376897.3377859

Page 2: Improving Resource Efficiency of Deep Activity Recognition

I m p r o vi n g R e s o u r c e E ffi ci e n c y of D e e p A cti vit y R e c o g niti o n vi aR e d u n d a n c y R e d u cti o n

Cl a yt o n Fr e d eri c k S o u z a L eit eA alt o U ni v ersit yEs p o o, Fi nl a n d

cl a yt o n.s o u z al eit e @ a alt o. fi

Y u Xi a oA alt o U ni v ersit yEs p o o, Fi nl a n d

y u. xi a o @ a alt o. fi

A b st r a ct

C o m pr essi o n m et h o ds f or d e e p l e ar ni n g h a v e b e e n r e c e ntl y us e d t op ort d e e p n e ur al n et w or ks i nt o r es o ur c e- c o nstr ai n e d d e vi c es - s u c has di git al gl o v es a n d s m art w at c h es - f or h u m a n a cti vit y r e c o g niti o n( H A R). W hil e t h e r es ults h a v e b e e n i n f a v or of utili zi n g c o m pr ess e dm o d els, w e e n visi o n t h at t h e c urr e nt p ar a di g m of l o n g a n d fi x e d-si z eo v erl a p pi n g sli di n g wi n d o ws t h at p er m e at e t h e lit er at ur e of H A Rc o ntri b ut es n e g ati v el y t o w ar d t h e g o al of m or e r es o ur c e- e ffi ci e nts yst e ms, as it i n d u c es r e d u n d a n ci es i n m e m or y a n d c o m p ut ati o n.I n t his w or k, w e pr o vi d e a di ff er e nt p ers p e cti v e b y d e m o nstr ati n gt h at m e m or y f o ot pri nt, c o m p ut ati o n al e x p e ns e, a n d p ossi bl y e n-er g y c o ns u m pti o n c a n b e dr a m ati c all y s p ar e d b y m o dif yi n g t h ear c hit e ct ur e of t h e n e ur al n et w or ks a n d t h eir tr ai ni n g. It is a c hi e v e db y e n a bli n g n o n- o v erl a p pi n g s h ort sli di n g wi n d o ws a n d s ki p pi n gfi n e- gr ai n e d f e at ur es i n f a v or of r o u g h o n es o n c ert ai n o c c asi o ns,t h us r e d u ci n g t h e d e m a n d f or m or e p o w erf ul h ar d w ar e. C o m p ar e dwit h t h e st at e- of-t h e- art, o ur m et h o d is a bl e t o a c hi e v e c o m p ar a bl ep erf or m a n c e f ar m or e e ffi ci e ntl y i n t er ms of r es o ur c e us e.

C C S C o n c e pt s

• C o m p uti n g m et h o d ol o gi e s → M a c hi n e l e a r ni n g .

K e y w o r d s

H u m a n a cti vit y r e c o g niti o n; r es o ur c e- c o nstr ai n e d d e vi c es; d e e pl e ar ni n g

A C M R ef e r e n c e F o r m at:Cl a yt o n Fr e d eri c k S o u z a L eit e a n d Y u Xi a o. 2 0 2 0. I m pr o vi n g R es o ur c e E ffi-

ci e n c y of D e e p A cti vit y R e c o g niti o n vi a R e d u n d a n c y R e d u cti o n. I n Pr o c e e d-i n gs of t h e 2 1st I nt er n ati o n al Wor ks h o p o n M o bil e C o m p uti n g S yst e ms a n dA p plic ati o ns ( H ot M o bil e ’ 2 0), M arc h 3 – 4, 2 0 2 0, A usti n, T X, U S A. A C M, N e w

Y or k, N Y, U S A, 6 p a g es. htt ps:// d oi. or g/ 1 0. 1 1 4 5/ 3 3 7 6 8 9 7. 3 3 7 7 8 5 9

1 I nt r o d u cti o n

H u m a n a cti vit y r e c o g niti o n ( H A R) is a k e y el e m e nt i n w e ar a bl ea p pli c ati o ns s u c h as fit n ess tr a c ki n g, str o k e r e h a bilit ati o n, a n de m pl o y e e tr ai ni n g. It c o nsists i n i nf erri n g fr o m s e ns or d at a t h et y p e of a cti o ns a p ers o n is e x e c uti n g. C urr e ntl y, s h all o w m a c hi n el e ar ni n g al g orit h ms ar e b ei n g o v ers h a d o w e d b y d e e p l e ar ni n g ( D L)

P er missi o n t o m a k e di git al or h ar d c o pi es of all or p art of t his w or k f or p ers o n al orcl assr o o m us e is gr a nt e d wit h o ut f e e pr o vi d e d t h at c o pi es ar e n ot m a d e or distri b ut e df or pr o fit or c o m m er ci al a d v a nt a g e a n d t h at c o pi es b e ar t his n oti c e a n d t h e f ull cit ati o no n t h e first p a g e. C o p yri g hts f or c o m p o n e nts of t his w or k o w n e d b y ot h ers t h a n A C Mm ust b e h o n or e d. A bstr a cti n g wit h cr e dit is p er mitt e d. T o c o p y ot h er wis e, or r e p u blis h,t o p ost o n s er v ers or t o r e distri b ut e t o lists, r e q uir es pri or s p e ci fi c p er missi o n a n d / or af e e. R e q u est p er missi o ns fr o m p er missi o ns @ a c m. or g.

H ot M o bil e ’ 2 0, M arc h 3 – 4, 2 0 2 0, A usti n, T X, U S A

© 2 0 2 0 Ass o ci ati o n f or C o m p uti n g M a c hi n er y.A C M I S B N 9 7 8- 1- 4 5 0 3- 7 1 1 6- 2/ 2 0/ 0 3.htt ps:// d oi. or g/ 1 0. 1 1 4 5/ 3 3 7 6 8 9 7. 3 3 7 7 8 5 9

i n H A R f or t w o r e as o ns. First, D L al g orit h ms pr o vi d e si g ni fi c a ntl e a ps i n p erf or m a n c e. S e c o n d, wit h f e at ur e l e ar ni n g, D L dr o ps t h er e q uir e m e nt f or h a n d- e n gi n e er e d f e at ur es o bt ai n e d fr o m e x p ertk n o wl e d g e.

Hi g h p erf or m a n c e a n d f e at ur e l e ar ni n g ar e att ai n e d b y usi n gs e v er al st a c k e d l a y ers wit h i n n u m er a bl e c o m p ut ati o ns t h at gr a d u-all y tr a nsf or m t h e r a w d at a i nt o t h e d esir e d o ut p ut. T h e n u m b erof p ar a m et ers i n t h e l a y ers c a n r e a c h b e y o n d milli o ns. H e n c e, D Lm o d els p oss ess a hi g h m e m or y f o ot pri nt, c o m p ut ati o n e x p e ns e, a n de n er g y c o ns u m pti o n. As a n e x a m pl e, a si m pl e C N N- L S T M m o d elf or H A R c a n r e q uir e a b o v e 4 0 0 K B of m e m or y a n d 3 0 0 M F L O Ps ofc o m p ut ati o n p er pr e di cti o n, w hi c h is b e y o n d t h e s p e ci fi c ati o ns ofp o p ul ar o ff-t h e-s h elf mi cr o c o ntr oll ers t h at ar e c o m m o nl y us e d f orw e ar a bl e d e vi c es d u e t o t h eir s m all si z e a n d l o w c ost.

T h e m e m or y f o ot pri nt of D L n et w or ks li mits t h eir s uit a bilit y o nm e m or y-s c ar c e a p pli a n c es. Als o, d es pit e mi cr o c o ntr oll ers h a vi n gr e a c h e d hi g h er c o m p ut ati o n c a p a biliti es o v er t h e p ast y e ars, t h ee x e c uti o n ti m e of d e e p m o d els c a n still t a k e l o n g e n o u g h t o bri n gd el a ys - e v e n i n t h e or d er of s e c o n ds - t o t h e e n d- us er a n d h ar mt h e o v er all us er e x p eri e n c e. F urt h er m or e, mi ni mi zi n g e n er g y c o n-s u m pti o n is pri m or di al t o s atisf y e n d- us ers, si n c e d e vi c es wit h s h orta ut o n o m y c a n b e s e e n as b ur d e ns o m e. R u n ni n g t h e D L al g orit h mso n t h e cl o u d is a n alt er n ati v e, b ut n ot al w a ys d esir e d, b y c a us eof pri v a c y-r el at e d iss u es a n d a d diti o n al o v er h e a d d u e t o l at e n c yi n c o m m u ni c ati o ns. T o o v er c o m e t h es e iss u es, m u c h r es e ar c h at-t e nti o n h as b e e n dr a w n t o p orti n g D L al g orit h ms i nt o w e ar a bl ed e vi c es t h at ar e r es o ur c e- c o nstr ai n e d.

R e m o vi n g r e d u n d a n ci es is t h e ess e n c e i n p orti n g D L al g orit h msf or H A R i nt o d e vi c es of li mit e d r es o ur c es. G e n eri c c o m pr essi o nm et h o ds [ 2 , 3 , 5 , 8 , 1 1 ] h a v e b e e n e xt e nsi v el y us e d t o m a k e d e e pm o d els m e et t h e m e m or y, c o m p ut ati o n, a n d e n er g y c o ns u m pti o nr e q uir e m e nts of s m all d e vi c es b y r e d u ci n g t h e r e d u n d a n c y i n t h e p a-r a m et ers of t h e n e ur al n et w or k. H o w e v er, w e e n visi o n t h at t h e c ur-r e nt p ar a di g m of H A R s yst e ms pr es e nts t hr e e a d diti o n al o v erl o o k e dr e d u n d a n ci es t h at ar e y et t o b e a d dr ess e d a n d, i n c o m bi n ati o n wit hc o m pr essi o n m et h o ds (s u c h as w ei g ht pr u ni n g, q u a nti z ati o n, a n df a ct ori z ati o n), c a n p ot e nti all y r e a c h e v e n hi g h er l e v els of r es o ur c ee ffi ci e n c y t h a n t h e s ol e us e of c o m pr essi o n m et h o ds. T h es e o v er-l o o k e d r e d u n d a n ci es ar e pr es e nt e d as f oll o ws.1) O v e rl a p pi n g sli di n g wi n d o w s. T h e b est- p erf or mi n g m et h o dso n v ari o us H A R b e n c h m ar ks ( as I n n o H A R [ 1 3 ]) still utili z e o v er-l a p pi n g sli di n g wi n d o ws. W h e n tr ai ni n g, s e g m e nti n g t h e d at as etwit h o v erl a p pi n g wi n d o ws s er v es as d at a a u g m e nt ati o n a n d c o n-cr et el y h el ps t h e n e ur al n et w or k i n g e n er ali zi n g b ett er. H o w e v er,w h e n t esti n g, h a vi n g o v erl a p pi n g wi n d o ws l e a ds t o c o m p ut ati o n alr e d u n d a n c y si n c e t h e s a m e p orti o n of d at a is pr o c ess e d m or e t h a no n c e.

Page 3: Improving Resource Efficiency of Deep Activity Recognition

H ot M o bil e ’ 2 0, M ar c h 3 – 4, 2 0 2 0, A u sti n, T X, U S A Cl a yt o n Fr e d eri c k S o u z a L eit e a n d Y u Xi a o

2) L o n g sli di n g wi n d o w s. W hil e l o n g sli di n g wi n d o ws pr o vi d ea wi d er t e m p or al c o nt e xt l e a di n g t o m or e a c c ur at e pr e di cti o ns,t h e y als o si g ni fi c a ntl y i n cr e as e t h e n u m b er of p ar a m et ers i n c ert ai nt y p es of l a y ers (s u c h as r e c urr e nt a n d f ull y- c o n n e ct e d ( F C) l a y ers)i n a n e ur al n et w or k. T his is m e m or y r e d u n d a n c y. As w e will s e e i nS e cti o n 3, l o n g sli di n g wi n d o ws d o n ot h el p i n o bt ai ni n g q ui c k erpr e di cti o ns.3) C o n s e c uti v e p r e di cti o n s of t h e s a m e a cti vit y. If t h e s a m ea cti vit y is p erf or m e d f or a l o n g p eri o d, t h e D L al g orit h m will b er e d u n d a ntl y e xtr a cti n g t h e s a m e f e at ur es f or t h e w h ol e d ur ati o n oft h e a cti vit y. R e m o vi n g t his c o m p ut ati o n al r e d u n d a n c y c a n l e a d t oa n a p pr e ci a bl e r e d u cti o n of r es o ur c e utili z ati o n.

I n t his p a p er, w e i n v esti g at e t o w h at e xt e nt t h e af or e m e nti o n e dr e d u n d a n ci es a ff e ct r es o ur c e utili z ati o n a n d t h e p ot e nti al i m pr o v e-m e nt g ai n e d b y a d dr essi n g t h e m wit h a n o v el m et h o d pr o p os e d b yus. First, w e dr o p t h e n e e d f or o v erl a p pi n g wi n d o ws b y a c h a n g ei n t h e tr ai ni n g of t h e n et w or k, a n d w e si g ni fi c a ntl y s h ort e n t h esli di n g wi n d o ws t o mi ni mi z e m e m or y f o ot pri nt a n d t o d et e ct a cti v-iti es as s o o n as p ossi bl e. S e c o n d, w e utili z e r o u g h f e at ur es a n d s ki pfi n e- gr ai n e d o n es f or g e n er ati n g pr e di cti o ns d uri n g t h e e x e c uti o nof l o n g-l asti n g a cti viti es. Wit h sli g ht p erf or m a n c e d e cr e as e, o urs ol uti o ns ar e a bl e t o a c hi e v e c o m p ar a bl e p erf or m a n c e wit h t h est at e- of-t h e- art wit h si g ni fi c a ntl y f e w er n et w or k p ar a m et ers a n dc o m p ut ati o n al c ost.

T h e r est of t h e p a p er is or g a ni z e d as f oll o ws. S e cti o n 2 i ntr o d u c est h e r el at e d w or k of H A R. S e cti o n 3 pr es e nts t h e pr o p os e d m et h o ds.S e cti o n 4 d es cri b es t h e d at as ets, wit h t h e pr eli mi n ar y e x p eri m e nt alr es ults pr es e nt e d i n S e cti o n 5 al o n g wit h a dis c ussi o n a b o ut t h e ma n d f ut ur e w or k. S e cti o n 6 pr es e nts t h e c o n cl usi o n.

2 R el at e d W o r k

H A R wit h D L. Or d o n e z a n d R o g g e n [ 1 0 ] c o m bi n e d c o n v ol uti o n ala n d r e c urr e nt L o n g S h ort- Ter m M e m or y ( L S T M) l a y ers i n a si n gl en et w or k f or p erf or mi n g H A R. G u a n et al. [ 7] w or k e d wit h e ns e m-bl es of L S T M n et w or ks f or H A R. T h e y h a v e s h o w n t h at si g ni fi c a ntp erf or m a n c e i m pr o v e m e nt ( u p t o 1 0 %) c a n b e a c hi e v e d usi n g e n-s e m bl es of r e c urr e nt n et w or ks i nst e a d of a si n gl e o n e. L o n g et al. [9 ]e m pl o y e d r esi d u al n et w or ks wit h c o n v ol uti o n al a n d L S T M l a y erst o c a pt ur e s p ati al a n d fi n e t e m p or al f e at ur es, r es p e cti v el y. T h eirm o d el r e a c h e d hi g h er p erf or m a n c e c o m p ar e d t o s e v er al si m pl ero n es w h e n t est e d o n p u bli c d at as ets wit h a cti viti es li k e st a n di n g u p,l yi n g d o w n, r u n ni n g, et c. X u et al. [1 3 ] h a v e a c hi e v e d st at e- of-t h e-art p erf or m a n c e o n t h e O p p ort u nit y [ 4 ] a n d P A M A P 2 [1 2 ] d at as etsb y pr o p osi n g a n e ur al n et w or k wit h I n c e pti o n-li k e c o n v ol uti o n all a y ers f oll o w e d b y g at e d r e c urr e nt u nit ( G R U) l a y ers. O v er all, D Lh as b e e n a p pli e d t o H A R wit h a n e n or m o us v ari et y of m et h o ds,wit h e a c h of w hi c h d eli v eri n g e v er-i n cr e asi n g p erf or m a n c e s c or es.H o w e v er, t h es e m et h o ds still utili z e l o n g a n d fi x e d-si z e o v erl a p pi n gsli di n g wi n d o ws t h at r e n d er t h eir i m pl e m e nt ati o n o n r es o ur c e-c o nstr ai n e d d e vi c es di ffi c ult.D L i n r e s o u r c e- c o n st r ai n e d d e vi c e s. L a n e et al. [8 ] cr e at e d at o ol kit t h at a p pli es t o a n y e xisti n g D N N or C N N m o d el 5 di ff er e ntt y p es of c o m pr essi o n (i. e., s p ars e f a ct ori z ati o n, w ei g ht f a ct ori z a-ti o n, pr e cisi o n r e d u cti o n, c o n v ol uti o n s e p ar ati o n, a n d p ar a m et ercl e a ni n g) g e n er ati n g a n e w c o m pr ess e d m o d el wit h sli g htl y l o w er

a c c ur a c y b ut si g ni fi c a nt i m pr o v e m e nts i n m e m or y f o ot pri nt, e x e-c uti o n ti m e a n d e n er g y c o ns u m pti o n. T o r e d u c e e x e c uti o n ti m esof d e e p n e ur al n et w or ks o n e m b e d d e d d e vi c es, Y a o et al. [1 4 ] d e-v el o p e d a fr a m e w or k t h at s p ots l a y ers t h at t a k e l o n g er t o r u n a n dc o n c e ntr at es e xisti n g c o m pr essi o n al g orit h ms o n t h e m, i nst e a dof tr e ati n g all l a y ers e q u all y. T h eir m et h o d pr o vi d e d s h ort er e x-e c uti o n ti m e a n d s m all er e n er g y c o ns u m pti o n, c o m p ar e d t o t h est at e- of-t h e- art c o m pr essi o n al g orit h ms.

Ali p pi et al. [1 ] r es ort e d t o a p pr o xi m at e c o m p uti n g t e c h ni q u est o r e d u c e c o m p ut ati o n al l o a d a n d m e m or y f o ot pri nt of C N N n et-w or ks o n e m b e d d e d d e vi c es. F a n g et al. [6 ] d e v el o p e d a t o ol t h atc o nti n u o usl y m o nit ors r es o ur c e a v ail a bilit y i n s yst e ms t h at r u nm ulti pl e a p pli c ati o ns c o n c urr e ntl y a n d s wit c h es b et w e e n di ff er e ntc o m pr ess e d v ersi o ns of t h e s a m e n e ur al n et w or k d e p e n di n g o n t h er es o ur c e s c ar cit y.

E x c e pt f or utili zi n g c o m pr ess e d n et w or ks, r es o ur c e c o ns u m pti o nc a n p ot e nti all y b e f urt h er r e d u c e d b y r e m o vi n g t h e r e d u n d a n ci esm e nti o n e d i n S e cti o n 1, w hi c h h as n ot b e e n st u di e d pr e vi o usl y.M oti v at e d b y t his, w e dr a w o ur att e nti o n t o t h es e r e d u n d a n ci esa n d pr o p os e w a ys t o a d dr ess t h e m.

3 M et h o d s

We pr o p os e t w o a p pr o a c h es: s h ort n o n- o v erl a p pi n g sli di n g wi n-d o ws a n d r o u g h f e at ur es f or pr e di cti n g l o n g-l asti n g a cti viti es. T h ef or m er is us e d t o a d dr ess t h e first a n d s e c o n d r e d u n d a n ci es dis-c uss e d i n S e cti o n 1. T h e l att er a d dr ess es t h e t hir d o n e. T h es e t w oa p pr o a c h es ar e i n d e p e n d e nt of e a c h ot h er, h e n c e, c a n b e eit h er us e ds e p ar at el y or t o g et h er.

3. 1 S h o rt n o n- o v e rl a p pi n g sli di n g wi n d o w s

D uri n g t h e tr ai ni n g p h as e, usi n g o v erl a p pi n g wi n d o ws s er v es as a ne ff e cti v e d at a a u g m e nt ati o n pr o c ess, as it r es ults i n cl assi fi c ati o nal g orit h ms b ei n g l ess bi as e d t o t h e e x a ct p ositi o n w h er e c ert ai nc h ar a ct eristi cs ( e. g., p e a ks a n d v all e ys i n t h e s e ns or y r e a di n gs) a p-p e ar i n t h e d at a. D uri n g t h e t esti n g p h as e, t h e y s er v e t h e n e ur aln et w or k wit h a br o a d er t e m p or al c o nt e xt t o pr e di ct t h e a cti vit ym or e a c c ur at el y. H o w e v er, t his si g ni fi es t h at t h e s a m e p orti o n ofd at a is pr o c ess e d m or e t h a n o n c e, w hi c h c a us es a w ast e of c o m p u-t ati o n al r es o ur c es.

R e c urr e nt l a y ers as L S T M or G R U h a v e b e e n e xt e nsi v el y us e dt o c a pt ur e t e m p or al c o nt e xt i n H A R. H o w e v er, a ut h ors li mit e d t h etr ai ni n g of t h es e l a y ers t o gr as p t h e t e m p or al c o nt e xt o nl y c o n-t ai n e d i nsi d e t h e sli di n g wi n d o w, disr e g ar di n g t h e t e m p or al c o nt e xte xist e nt a cr oss sli di n g wi n d o ws. We c all t h e f or m er i nt r a- wi n d o wt e m p o r al c o nt e xt a n d t h e l att er i nt e r- wi n d o w t e m p o r al c o n-t e xt.

We pr o p os e a n ar c hit e ct ur e c o m p os e d of c o n v ol uti o n al l a y erst h at l e ar n s p ati al a n d i ntr a- wi n d o w t e m p or al c o nt e xt, f oll o w e d b ya n L S T M l a y er li mit e d t o gr as pi n g i nt er- wi n d o w t e m p or al c o nt e xt.T o a c hi e v e t his, w e c oll a ps e t h e t hr e e- di m e nsi o n al f e at ur e m a psgi v e n b y t h e l ast c o n v ol uti o n al d at a t o o n e di m e nsi o n (i nst e a d oft w o di m e nsi o ns). T h es e f e at ur e m a ps g o t hr o u g h t h e L S T M l a y erw hi c h is tr ai n e d usi n g b a c k pr o p a g ati o n t hr o u g h t h e f e at ur e m a psof M pr e vi o us wi n d o ws, w h er e M is a n i nt e g er h y p er- p ar a m et er oft h e tr ai ni n g. T his m e a ns t h at t h e L S T M l a y er is tr ai n e d t o b e a bl et o r e c all i nf or m ati o n fr o m pr e vi o us wi n d o ws, t h us r e m o vi n g t h e

Page 4: Improving Resource Efficiency of Deep Activity Recognition

H ot M o bil e ’ 2 0, M ar c h 3 – 4, 2 0 2 0, A u sti n, T X, U S A

Fi g u r e 1: T h e a p p r o a c h of s h o rt n o n- o v e rl a p pi n g sli di n gwi n d o w s ( o n t h e ri g ht) c o m p a r e d t o t h e lit e r at u r e’ s a p-p r o a c h ( o n t h e l eft). x k i s t h e k-t h sli di n g wi n d o w si n c e t h eb e gi n ni n g of t h e d at a st r e a m. I n o u r m et h o d, a p r e di cti o n y ki s i g n o r e d if it s c o n fi d e n c e l e v el i s b el o w a c e rt ai n t h r e s h ol d.I n b ot h m et h o d s, f o r e v e r y sli di n g wi n d o w, a p r e di cti o n i sgi v e n.

n e e d f or r e p e ati n g pr e vi o usl y s e e n i nf or m ati o n - i. e. r e m o vi n g t h en e e d f or o v erl a p pi n g wi n d o ws.

T h e n e e d f or usi n g a l o n g sli di n g wi n d o w c o m es fr o m t h e i d e at h at h a vi n g a n a p pr e ci a bl e a m o u nt of d at a is b e n e fi ci al f or pr o-d u ci n g m or e a c c ur at e pr e di cti o ns si n c e t h e cl assi fi er is f e d wit h abr o a d er t e m p or al c o nt e xt. H a vi n g est a blis h e d t h at o ur L S T M l a y eris r e q uir e d t o l e ar n i nt er- wi n d o w t e m p or al c o nt e xt, t h e n e e d f orl o n g sli di n g wi n d o ws is r e m o v e d. We c a n s h ort e n t h e wi n d o w si z ea n d a p pr e ci a bl y d e cr e as e t h e n u m b er of p ar a m et ers i n t h e L S T Ma n d F C l a y ers. Fi g. 1 ill ustr at es t his a p pr o a c h c o m p ar e d t o h o w it isc urr e ntl y d o n e i n t h e lit er at ur e.

S h ort er sli di n g wi n d o ws als o h el p i n pr e di cti n g n e w i n c o mi n ga cti viti es q ui c k er. E a c h sli di n g wi n d o w c a n b e l a b el e d eit h er wit ht h e a cti vit y t h at is pr es e nt i n t h e l ar g est p orti o n of t h e wi n d o wor wit h t h at s e e n at t h e e n d of it. T h e f or m er a p pr o a c h is m or ewi d el y us e d. W h e n usi n g l o n g wi n d o ws, e v e n if a n e w a cti vit y isalr e a d y s h o wi n g u p at t h e e n d of t h e wi n d o w, t h e n e ur al n et w or kswill b e tr ai n e d t o i g n or e it i n f a v or of t h e o n e t h at still d o mi n at est h e wi n d o w. Wit h t his, t h e pr e di cti o n of n e w i n c o mi n g a cti viti es isd el a y e d. T h e l att er a p pr o a c h i ntr o d u c es n ois y l a b els a ff e cti n g t h ep erf or m a n c e a d v ers el y si n c e m ost of t h e i nf or m ati o n c o nt ai n e d i nt h e wi n d o w is n ot r el at e d t o t h e u p c o mi n g a cti vit y. T h e e xist e n c eof n ois y l a b els is m or e pr o n o u n c e d w h e n usi n g l o n g er wi n d o ws,as t h e o d ds of h a vi n g m or e t h a n o n e a cti vit y i n a si n gl e wi n d o w ishi g h er.

Usi n g s h ort er wi n d o ws r e d u c es t h e i n ci d e n c es of n ois y l a b elsa n d, als o i m p ort a ntl y, h el ps i n o ur g o al of h a vi n g c o n v ol uti o n all a y ers t h at l e ar n t h e i ntr a- wi n d o w t e m p or al c o nt e xt. T his is tr u eb e c a us e c o n v ol uti o n al l a y ers r el y o n t h e c o n c e pt of l o c alit y a n dh a v e di ffi c ulti es l e ar ni n g dist a nt d e p e n d e n ci es i n t h e d at a.

Wit h s h ort er sli di n g wi n d o ws, pr e di cti o ns m a y b e err o n e o usri g ht at t h e m o m e nt of tr a nsiti o n - t h at is, t h e b e gi n ni n g of a n e wa cti vit y - si n c e t h e n e ur al n et w or k m a y n ot h a v e b e e n pr es e nt e dwit h e n o u g h i nf or m ati o n t o a c c ur at el y r e c o g ni z e t h e u p c o mi n ga cti vit y. T h er ef or e, w e pr o p os e t o disr e g ar d pr e di cti o ns wit h l o wc o n fi d e n c e s c or es ( w hi c h is c al c ul at e d b y t a ki n g t h e m a xi m u m v al u e

Fi g u r e 2: N et w o r k a r c hit e ct u r e. D u ri n g i nf e r e n c e, t h e i n p utx k + n a n d, c o n s e q u e ntl y, t h e p r e di cti o n y k = y k + n a r e n otp r e s e nt.

i n t h e v e ct or o ut p ut b y t h e n et w or k). T his si g ni fi es t h at pr e di cti o ny i ,k is i g n or e d if y i ,k < P t h , wit h P t h b ei n g a h y p er- p ar a m et err ef err e d t o as t h e c o n fi d e n c e t hr es h ol d. I n a c ert ai n s e ns e, t his ise q ui v al e nt t o h a vi n g a d y n a mi c sli di n g wi n d o w, si n c e pr e di cti o nsm a y c o m e at n o n- fi x e d i nt er v als of ti m e.

We utili z e t h e us u al cr oss- e ntr o p y l oss f or tr ai ni n g t h e n et w or k.We e m p h asi z e t h at t h e tr ai ni n g, v ali d ati o n a n d t est s ets d o n’t p os-s ess o v erl a p pi n g wi n d o ws. Als o, t h e wi n d o ws c a n b e as s h ort as0. 1 s e c o n ds.

3. 2 P r e di cti o n wit h r o u g h f e at u r e s

D e n oti n g as x k a sli di n g wi n d o w at i nst a nt k a n d y k its l a b el, s u p-p os e a n a cti vit y h as b e e n pr e di ct e d wit h a hi g h c o n fi d e n c e s c or eat i nst a nt k , t h e o d ds ar e hi g h t h at s u bs e q u e nt wi n d o ws c o nt ai nt h e s a m e a cti vit y. As t h e n e ur al n et w or k c o nti n u es t o pr e di ct t hiss a m e a cti vit y a cr oss s u c c essi v e sli di n g wi n d o ws, t h e s a m e f e at ur esar e r e d u n d a ntl y e xtr a ct e d. T his l e a ds t o a mis us e of c o m p ut ati o n.

T o a d dr ess t his iss u e, w e pr o p os e a n a d diti o n al n e ur al n et w or kof l o w- c o m pl e xit y r ef err e d t o as t h e s e c o n d a r y n et w or k. D uri n gi nf er e n c e ti m e, gi v e n a wi n d o w x k , t h e m ai n n et w or k e xtr a ctsits r o u g h f e at ur es N R (x k ) w hi c h ar e c o n c at e n at e d i n t h e c h a n n eldi m e nsi o n wit h N R (x k − 1 ). T h e r es ult of t h e c o n c at e n ati o n is p ass e dt hr o u g h t h e s e c o n d ar y n et w or k t h at t ells w h et h er or n ot y k = y k − 1 .I n c as e t his is tr u e a n d t h e c o n fi d e n c e of t his pr e di cti o n is hi g h ert h a n a c ert ai n c o n fi d e n c e t hr es h ol d, fi n e- gr ai n e d f e at ur es a n d t h eirs u bs e q u e nt cl assi fi c ati o n ar e s ki p p e d. I n t h e o p p osit e c as e, t h e m ai nn et w or k N f oll o ws its e x e c uti o n u ntil t h e cl assi fi c ati o n t o o bt ai ny k . T h us, t h e s e c o n d ar y n et w or k c o nsists i n a bi n ar y cl assi fi er t h ato ut p uts 1 if y k = y k − 1 or, ot h er wis e, 0 . We d e n ot e t his n et w or k asa f u n cti o n D (·) a n d N R (x k ) as t h e r o u g h f e at ur es e xtr a ct e d b y t h em ai n n et w or k N . O n e of t h e l oss f u n cti o ns f or t h e tr ai ni n g of t h es e c o n d ar y n et w or k is gi v e n i n E q. 1.

L C E C W = −1

B − 1

B

k = 2

д k ,k − 1 l o g(д k ,k − 1 )

− ( 1 − д k ,k − 1 ) l o g(1 − д k ,k − 1 ),

( 1)

w h er e B is t h e n u m b er of wi n d o ws i n t h e tr ai ni n g s et, д k ,k − 1 =D (N R (x k ) ⊕ N R (x k − 1 )) ( h er e t h e s y m b ol ⊕ d e n ot es c o n c at e n ati o nof t h e 3 D f e at ur es al o n g t h e c h a n n el di m e nsi o n) a n d д k ,k − 1 = 1 ify k = y k − 1 or д k ,k − 1 = 0 ot h er wis e.

Tr ai ni n g t h e s e c o n d ar y n et w or k o nl y wit h L C E C W l e a ds t o abi as e d cl assi fi er, si n c e h a vi n g д k ,k − 1 = 1 is m u c h m or e c o m m o n

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H ot M o bil e ’ 2 0, M ar c h 3 – 4, 2 0 2 0, A u sti n, T X, U S A Cl a yt o n Fr e d eri c k S o u z a L eit e a n d Y u Xi a o

t h a n t h e o p p osit e. T h er ef or e, t o b al a n c e t h e tr ai ni n g, w e pr o p os et o mi ni mi z e a n a d diti o n al l oss f u n cti o n ( E q. 2).

L C E R W = −1

B

B

k = 1

д k ,k + n l o g(д k ,k + n )

− ( 1 − д k ,k + n ) l o g(1 − д k ,k + n ),

( 2)

w h er e n is a n ar bitr ar y i nt e g er d e fi n e d s u c h t h at y k + n y k t ob al a n c e t h e cl assi fi c ati o n.

N at ur all y, t h e b e n e fits c o m e o nl y w h e n t h e s e c o n d ar y n et w or kpr e di cts t h at t h e s a m e a cti vit y is o c c urri n g si n c e t h e n e e d f or p er-f or mi n g m or e c o m pl e x c o m p ut ati o ns ( as t h os e i n t h e L S T M l a y er)is a v oi d e d. O v er all, si n c e c h a n g es i n a cti viti es o c c ur wit h l o w fr e-q u e n c y i n H A R, t h e us e of t h e s e c o n d ar y n et w or k h el ps t o r e d u c et h e c o m p ut ati o n al l o a d as l o n g as its c osts ar e l ess t h a n t h at ofe xtr a cti n g m or e fi n e- gr ai n e d f e at ur es a n d cl assif yi n g t h e m. T o h el pt o w ar ds t his g o al, w e d esi g n t h e s e c o n d ar y n et w or k wit h c o n v ol u-ti o ns of s h ort k er n el si z es a n d a s ol e c o n v ol uti o n al l a y er.

Fi n all y, t h e e ntir e tr ai ni n g of t h e n et w or ks ( d e pi ct e d i n Fi g. 2)c a n b e s u m m ari z e d b y mi ni mi zi n g t h e l oss f u n cti o n L = L C E +L C E C W + L C E R W , w h er e L C E is t h e us u al cr oss- e ntr o p y l oss f u n c-ti o n.

4 D at a s et s

T o e v al u at e o ur m et h o d, w e utili z e t h e P A M A P 2 [ 1 2 ] a n d O p p or-t u nit y [4 ] d at as ets. All s a m pl es i n b ot h d at as ets ar e n or m ali z e dt o z er o m e a n a n d u nit v ari a n c e. We f oll o w pr e vi o us w or ks i n t h es el e cti o n of tr ai ni n g, v ali d ati o n a n d t est s ets ( d et ail e d as f oll o ws)t o pr o vi d e m e a ns of e asil y c o m p ari n g o ur pr o p os al wit h t h eirs.P A M A P 2 . T h e P A M A P 2 d at as et c o nt ai ns 1 8 di ff er e nt p h ysi c al a c-ti viti es p erf or m e d b y 9 p arti ci p a nts w e ari n g a h e art m o nit or a n dt hr e e I M Us att a c h e d t o t h e c h est, h a n d, a n d a n kl e, r es p e cti v el y. O utof t h e 1 8 a cti viti es, 6 ar e r ar el y pr es e nt i n t h e d at a. T o a v oi d h a vi n ga h e a vil y i m b al a n c e d d at as et, o nl y t h e r e m ai ni n g 1 2 a cti viti es ar ec o nsi d er e d i n o ur e x p eri m e nts: l yi n g q ui etl y, sitti n g, st a n di n g, ir o n-i n g, v a c u u m cl e a ni n g, as c e n di n g st airs, d es c e n di n g st airs, w al ki n g,N or di c w al ki n g, bi c y cli n g, r u n ni n g, a n d r o p e j u m pi n g. T h e m e a nd ur ati o n is 1 mi n ut e f or as c e n di n g a n d d es c e n di n g st airs, 2 mi n-ut es f or r o p e j u m pi n g a n d 3 mi n ut es f or t h e r e m ai ni n g a cti viti es.Missi n g v al u es o n t h e d at a ( N a N v al u es) ar e dis c ar d e d. Als o, t h es a m pli n g r at e is d e ci m at e d fr o m 1 0 0 H z t o 3 3. 3 H z. T h e e ntir e d at afr o m s u bj e ct 5 a n d s u bj e ct 6 w er e us e d, r es p e cti v el y, f or t h e v ali-d ati o n s et a n d t h e t esti n g s et. T his e ntir e pr ot o c ol is f oll o w e d i ndi v ers e w or ks, as i n [ 7].O p p o rt u nit y . T his d at as et i n c or p or at es 1 8 kit c h e n-r el at e d a cti v-iti es (t h e m e a n d ur ati o n of t h e a cti viti es is gi v e n i n p ar e nt h e-s es): cl e a ni n g a t a bl e ( 1 0s), o p e ni n g/ cl osi n g t h e fri d g e ( 3s), o p e n-i n g/ cl osi n g t h e dis h w as h er ( 3s), o p e ni n g/ cl osi n g 3 di ff er e nt dr a w ers( 2s), o p e ni n g/ cl osi n g 2 di ff er e nt d o ors ( 4s), t o g gli n g li g hts o n a n do ff ( 1s), a n d dri n ki n g fr o m a st a n di n g a n d sitti n g p ositi o n ( 1 0s). T h ed at a c oll e cti o n w as c arri e d o ut wit h 4 p arti ci p a nts usi n g 2 3 b o d y-w or n s e ns ors. I n o ur e x p eri m e nts, t h e s a m pli n g r at e w as r e d u c e dt o 3 0 H z a n d o nl y t h e s e ns or y r e a di n gs fr o m t h e u p p er li m bs, t h eb a c k, a n d b ot h f e et w er e c o nsi d er e d ( as it w as als o d o n e i n [ 7 ]).Als o f oll o wi n g [ 7 ], t h e v ali d ati o n s et is c o m p os e d of s essi o n 1 fr o ms u bj e ct 2, w h er e as t h e t esti n g s et c o nsists of s essi o ns 2 a n d 3 fr o ms u bj e cts 4 a n d 5.

M et h o d F 1 N P A N P R T C E P D N A

P A M A P 2 d at as et

B as eli n e C N N- L S T M 0. 8 7 6 8 3. 6 5 K 3 1 4 1 3 3. 7 7 6. 1 6

C N N- L S T M S N 0. 9 1 2 5 1. 7 5 K 7 8 2 7 4. 6 0 1. 6 8

C N N- L S T M S N R 0. 8 8 2 5 3. 0 8 K 7 9 0 4 3. 9 9 2. 1 3

I n n o H A R [ 1 3] 0. 9 3 5 3 4. 9 1 M 3 1 4 1 1 8 8 7. 5 8 -

O p p ort u nit y d at as et

B as eli n e C N N- L S T M 0. 8 9 5 1 1 8. 4 6 K 7 4 1 6 3 5. 5 8 1. 0 3

C N N- L S T M S N 0. 9 5 9 8 4. 9 1 K 1 4 1 7 8 1 0. 0 7 0. 5 8

C N N- L S T M S N R 0. 8 9 3 8 7. 0 6 K 1 5 3 4 1 9. 0 6 0. 6 1

I n n o H A R [ 1 3] 0. 9 4 6 6. 2 0 M 9 8 9 0 8 7 3. 5 4 -

T a bl e 1: E x p e ri m e nt al r e s ult s. N P A, N P E a n d T C E f o r t h e I n-n o H A R m et h o d a r e a p p r o xi m ati o n s b a s e d o n t h e a r c hit e c-t u r e of t h e n et w o r k.

5 E v al u ati o n

5. 1 M et h o d s a n d h y p e r- p a r a m et e r s

We t est e d a b as eli n e C N N- L S T M n et w or k a n d v ari a nts of o urm et h o d: C N N- L S T M S N a n d C N N- L S T M S N R. T h e first v ari a nto nl y e m pl o ys b a c k pr o p a g ati o n t hr o u g h f e at ur e m a ps of pr e vi o ussli di n g wi n d o ws a n d s h ort sli di n g wi n d o ws. T h e S N R v ari a nt di ff ersfr o m t h e pr e vi o us as it als o e n c o m p ass es t h e cl assi fi c ati o n of l o n g-l asti n g a cti viti es wit h r o u g h f e at ur es. F or br e vit y, a v ari a nt of o nl yt h e a p pr o a c h of S e cti o n 3. 2 is n ot c o nsi d er e d.T h e b a s eli n e. T h e b as eli n e C N N- L S T M utili z es o v erl a p pi n g sli di n gwi n d o ws of 5. 1 2 s e c o n ds wit h 1-s e c o n d st e p si z e ( 7 8 % o v erl a p pi n g)f or P A M A P 2, w h er e as f or O p p ort u nit y t h e wi n d o ws h a v e a l e n gt hof 1 s e c o n d wit h 0. 5 s e c o n ds o v erl a p p e d ( 5 0 %). Pr e vi o us w or ks[7 , 9 , 1 0 , 1 3 ] h a v e als o us e d t h es e s etti n gs. T h e b as eli n e n et w or kc o nsists of 3 C N N l a y ers wit h 3 x 3 k er n el si z e, wit h e a c h C N N l a y erf oll o w e d b y a 2 x 2 m a x- p o oli n g l a y er. T h e o ut p ut of t h e C N N is fl at-t e n e d fr o m 3 D t o 2 D b ef or e b ei n g f e d t o t h e L S T M wit h 6 4 hi d d e nu nits. F oll o wi n g, t h er e ar e t w o F C l a y ers of 6 4 a n d C ( n u m b er ofcl ass es) n e ur o ns, r es p e cti v el y, b ef or e t h e fi n al s oft m a x l a y er.O u r m et h o d s. T h e s h ort er t h e wi n d o ws, t h e l o w er t h e m e m or yf o ot pri nt a n d c o m p ut ati o n e x p e ns e, b ut als o t h e l o n g er t h e i nt er-wi n d o w t e m p or al c o nt e xt t h at m ust b e l e ar n e d b y t h e L S T M l a y er.T h er e is a p oi nt at w hi c h f urt h er s h ort e ni n g of t h e wi n d o ws si g nif-i c a ntl y d e gr a d es t h e p erf or m a n c e d u e t o t h e i n a bilit y of t h e L S T Ml a y er i n m o d eli n g t h e l o n g er d e p e n d e n ci es it is r e q uir e d t o. We h a v ec h os e n wi n d o ws si z es of 0. 2 5 a n d 0. 1 8 s e c o n ds f or t h e P A M A P 2 a n dO p p ort u nit y d at as ets, r es p e cti v el y. T his s el e cti o n w as m a d e s ol el yai mi n g at mi ni mi zi n g t h e wi n d o w si z e wit h o ut n e g ati v e i m p a ct o nt h e v ali d ati o n p erf or m a n c e. We h a v e n ot c o nsi d er e d or i n v esti g at e dt h e e ff e ct of t h e m e a n d ur ati o n of t h e a cti viti es i n a d at as et i n t hiss el e cti o n. T h e n e ur al n et w or k h as t h e s a m e h y p er- p ar a m et ers ast h e b as eli n e, wit h t h e e x c e pti o n t h at t h e o ut p ut of t h e c o n v ol uti o nsis fl att e n e d fr o m 3 D t o 1 D b ef or e t h e L S T M l a y er. T h e c o n fi d e n c et hr es h ol d w as s et t o 0. 9. M w as s et b y tri al a n d err or t o 1 5 a n d 8 f orP A M A P 2 a n d O p p ort u nit y, r es p e cti v el y. T h e s e c o n d ar y n et w or k(i n t h e C N N- L S T M S N R v ari a nt) is c o m p os e d of o n e c o n v ol uti o n all a y er wit h 2 x 2 c o n v ol uti o ns a c c o m p a ni e d b y a m a x- p o oli n g l a y erof 2 x 2 k er n el si z e. T h e o ut p ut is fl att e n e d t o 1 D b ef or e a n F C l a y erwit h s oft m a x t h at o ut p uts 2 r e al n u m b ers. Als o f or t h e s e c o n d ar y

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H ot M o bil e ’ 2 0, M ar c h 3 – 4, 2 0 2 0, A u sti n, T X, U S A

n et w or k, t h e c o n fi d e n c e t hr es h ol d w as s et t o 0. 9 5. I n o ur m et h o ds,o nl y t h e tr ai ni n g s et is s e g m e nt e d i nt o o v erl a p pi n g wi n d o ws. E a c hwi n d o w h as 7 8 % a n d 5 0 % o v erl a p p e d wit h t h e pr e vi o us o n e f or t h eP A M A P 2 a n d O p p ort u nit y d at as ets, r es p e cti v el y.We us e d v ar yi n g l e ar ni n g r at es fr o m 1 e- 4 t o 5 e- 4 wit h A d a m o p-ti mi z ati o n al g orit h m. T h e c o d e w as writt e n i n P yt h o n 3 wit h t h eTe ns or fl o w 1. 1 2.

5. 2 M et ri c s

I n o ur e x p eri m e nts, w e m e as ur e d 5 di ff er e nt c h ar a ct eristi cs d e-s cri b e d b el o w.F 1- S c o r e ( F 1). T h e F 1- S c or e is a m or e m e a ni n gf ul p erf or m a n c em etri c t h a n t h e a c c ur a c y w h e n d e ali n g wit h i m b al a n c e d d at as ets,as it is us u all y t h e c as e i n H A R.N u m b e r o f p a r a m et e r s ( N P A). T h e t ot al n u m b er of p ar a m et ers( w ei g hts a n d bi as es) i n t h e n e ur al n et w or k.N u m b e r o f p r e di cti o n s ( N P R). T h e t ot al n u m b er of pr e di cti o nso v er t h e e ntir e t est s et. F or t h e S N a n d S N R v ari a nts, pr e di cti o nsar e dis c ar d e d if t h e c o n fi d e n c e l e v el is b el o w t h e c o n fi d e n c e t hr es h-ol d. T h es e dis c ar d e d pr e di cti o ns ar e n ot c o u nt e d t o t h e n u m b er ofpr e di cti o ns, h o w e v er, t h eir c o m p ut ati o n c ost is c o u nt e d t o t h e t ot alc o m p ut ati o n al e ff ort.T ot al c o m p ut ati o n al e ff o rt ( T C E). T ot al c o m p ut ati o n al e x p e ns e(i n G F L O Ps) i n pr e di cti n g a cti viti es o n t h e e ntir e t est s et.P r e di cti o n d el a y of n e w a cti viti e s ( P D N A). T h e a v er a g e n u m-b er of s e c o n ds of d at a n e e d e d t o a c c ur at el y pr e di ct a n e w a cti vit yo n c e it h as st art e d. It is c o u nt e d fr o m t h e m o m e nt t h e n e w a cti vit yis st art e d u p t o t h e m o m e nt w h e n it is a c c ur at el y pr e di ct e d.

W hil e t h e e x e c uti o n ti m e a n d e n er g y c o ns u m pti o n of a d e e pn e ur al n et w or k d o n ot s ol el y d e p e n d o n its n u m b er of p ar a m et ersa n d t ot al F L O Ps, t h es e m e as ur es c a n still b e us e d as r e as o n a bl yg o o d i n di c at ors f or t his p ur p os e.

5. 3 Di s c u s si o n

T a bl e 1 pr es e nts t h e r es ults of t h e d es cri b e d m et h o ds f or b ot hd at as ets. We als o i n cl u d e I n n o H A R [ 1 3 ] t h at c urr e ntl y r a n ks o n t h et o p of H A R b e n c h m ar ks f or t h e s a m e d at as ets.C N N- L S T M B a s eli n e v s. C N N- L S T M S N . Si g ni fi c a nt c o m p ut a-ti o n al e ff ort c a n b e s p ar e d b y l e ar ni n g i nt er- wi n d o w t e m p or al c o n-t e xt vi a a n L S T M l a y er i nst e a d of r es orti n g t o o v erl a p pi n g wi n d o ws.S h ort e ni n g t h e sli di n g wi n d o ws c o nsi d er a bl y d e cr e as es t h e n u m b erof n et w or k p ar a m et ers of t h e L S T M l a y er, as t h e i n p ut di m e nsi o n-alit y of t h e i n p ut of t h e L S T M l a y er als o d e cr e as es. Als o, w e o bt ai nt h e b e n e fit of s h ort er d el a ys i n r e c o g ni zi n g n e w a cti viti es. T h eC N N- L S T M S N o nl y o ut p uts pr e di cti o ns w h os e c o n fi d e n c e l e v el isa b o v e t h e d et er mi n e d c o n fi d e n c e t hr es h ol d. Cl ass es t h at ar e h ar d ert o b e pr e di ct e d ar e pr e di ct e d l ess oft e n t h a n t h e e asi er o n es si n c er e a c hi n g a hi g h e n o u g h c o n fi d e n c e l e v el f or t h e h ar d er cl ass esr e q uir es m or e d at a. T his r es ults i n a n i n cr e as e d p erf or m a n c e s c or e.C N N- L S T M S N v s. C N N- L S T M S N R . It is p ossi bl e t o t a k e a d v a n-t a g e of r o u g h f e at ur es t o pr e di ct l o n g-l asti n g a cti viti es. N at ur all y,t h e n u m b er of p ar a m et ers i n cr e as es wit h t h e a d diti o n of t h e s e c-o n d ar y n e ur al n et w or k, h o w e v er, t his als o l e a ds t o a f airl y s u bst a n-ti al r e d u cti o n i n c o m p ut ati o ns si n c e t h e L S T M l a y er is s ki p p e d o ns e v er al o c c asi o ns. Pr e di cti o ns ar e s e e n m or e oft e n as t h e s e c o n d ar yn et w or k o ut p uts pr e di cti o ns wit h c o n fi d e n c e a b o v e t h e t hr es h ol d

wit h o ut di ffi c ult y, as a bi n ar y cl assi fi c ati o n pr o bl e m is us u all y si m-pl er t h a n a m ulti cl ass pr o bl e m. T h e p erf or m a n c e, h o w e v er, s u ff ersa n e g ati v e i m p a ct, i n s p e ci al f or t h e O p p ort u nit y d at as et, w h os ea cti viti es l ast l ess t h a n 1 0s.O u r m et h o d v s. I n n o H A R . O ur m et h o d - eit h er t h e S N or S N Rv ari a nt - p oss ess es a m assi v el y f e w er n u m b er of p ar a m et ers a n dr e d u c es c o m p ut ati o n al e x p e ns e. P erf or m a n c e- wis e, o ur m et h o d isa bl e t o r a n k hi g h er ( wit h t h e S N v ari a nt) i n t h e O p p ort u nit y d at as et,w hil e h a vi n g si mil ar F 1-s c or e f or P A M A P 2. T h e P D N A m etri c f orI n n o H A R w as n ot c al c ul at e d, h o w e v er, it s h o ul d b e n e ar t h e b as eli n eas l o n g o v erl a p pi n g sli di n g wi n d o ws ar e als o us e d i n I n n o H A R.We b eli e v e t h at c o m bi ni n g eit h er of t h e m et h o ds pr o p os e d h er ewit h t h e i n c e pti o n-li k e str u ct ur e of t h e C N N l a y ers of I n n o H A Rc o ul d bri n g e v e n b ett er p erf or m a n c e wit h m u c h-i m pr o v e d r es o ur c ee ffi ci e n c y.

A d diti o n al r es o ur c e e ffi ci e n c y c o ul d b e g ai n e d b y utili zi n g dif-f er e nt l e v els of f e at ur es f or pr e di cti n g di ff er e nt a cti viti es. R o u g h erf e at ur es c o ul d b e us e d t o pr e di ct e as y-t o- pr e di ct a cti viti es, w hil em or e fi n e- gr ai n e d f e at ur es w o ul d b e us e d f or t h e h ar d er o n es.

We w o ul d als o li k e t o i n v esti g at e t o w h at e xt e nt o ur m et h o dsc a n i m pr o v e r es o ur c e e ffi ci e n c y o n a ct u al e m b e d d e d d e vi c es, b ye v al u ati n g t h e e ff e cts o n C P U a n d m e m or y s e p ar at el y, i nst e a d ofc o u nti n g o nl y t h e n u m b er of p ar a m et ers a n d t h e t ot al F L O Ps d ur-i n g i nf er e n c e. Fi n all y, w e ar e i nt er est e d i n c o m p ari n g o ur m et h o dwit h c o n v e nti o n al c o m pr essi o n m et h o ds a n d e v e n fi n di n g w a ys t oc o m bi n e t h e m f or a f urt h er i n cr e as e i n r es o ur c e e ffi ci e n c y.

6 C o n cl u si o n s

We h a v e s e e n t h at t h e m ost c o m m o n a p pr o a c h i n H A R is t o us e l o n ga n d fi x e d-si z e o v erl a p pi n g sli di n g wi n d o ws, w hi c h a ff e cts n e g a-ti v el y r es o ur c e e ffi ci e n c y. B y st u d yi n g t h e o v erl o o k e d r e d u n d a n ci esof t his a p pr o a c h, w e pr o p os e d t o us e s h ort n o n- o v erl a p pi n g sli di n gwi n d o ws a n d t o s ki p fi n e- gr ai n e d f e at ur es d uri n g l o n g-l asti n g a c-ti viti es. We us e a C N N- L S T M n e ur al n et w or k, w h os e L S T M l a y eris tr ai n e d t o l e ar n i nt er- wi n d o w t e m p or al c o nt e xt ( dr o p pi n g t h en e e d f or o v erl a p pi n g wi n d o ws) a n d t h e C N N l a y ers ar e d esi g n at e dt o l e ar n s p ati al a n d i ntr a- wi n d o w t e m p or al c o nt e xt. Wit h t his, o urpr o p os al is a bl e t o o bt ai n u p t o 3 5 % l ess n u m b er of p ar a m et ers i nt h e n et w or k a n d 8 x l ess c o m p ut ati o n wit h r es p e ct t o a b as eli n e,w hil e m ai nt ai ni n g hi g h p erf or m a n c e a n d pr e di cti n g n e w a cti viti esas s o o n as p ossi bl e. I n s u m m ar y, o ur pr o p os al d eli v ers c o m p ar a bl ep erf or m a n c e wit h t h e st at e- of-t h e- art w hil e b ei n g gr e atl y i m pr o v e di n t er ms of r es o ur c e e ffi ci e n c y.

A c k n o wl e d g m e nt s

T his w or k w as f u n d e d b y B usi n ess Fi nl a n d ( gr a nt N o. 1 6 6 0/ 3 1/ 2 0 1 8)a n d t h e E ur o p e a n U ni o n’s H ori z o n 2 0 2 0 R es e ar c h a n d I n n o v ati o nPr o gr a m m e ( gr a nt N o. 7 7 7 2 2 2).

R ef e r e n c e s

[ 1] C. Ali p pi, S. Dis a b at o, a n d M. R o v eri. 2 0 1 8. M o vi n g C o n v ol uti o n al N e ur al N et-w or ks t o E m b e d d e d S yst e ms: T h e Al e x n et a n d V G G- 1 6 C as e. I n I P S N ’ 1 8 ( P ort o,P ort u g al). I E E E Pr ess, Pis c at a w a y, NJ, U S A, 2 1 2 – 2 2 3.

[ 2] M. Ali z a d e h a n d N. D. L a n e. 2 0 1 8. Usi n g Pr e-tr ai n e d F ull- Pr e cisi o n M o d els t oS p e e d U p Tr ai ni n g Bi n ar y N et w or ks F or M o bil e D e vi c es. I n M o bi S ys ’ 1 8 ( M u ni c h,G er m a n y). A C M, N e w Y or k, N Y, U S A, 5 2 8 – 5 2 8.

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H ot M o bil e ’ 2 0, M ar c h 3 – 4, 2 0 2 0, A u sti n, T X, U S A Cl a yt o n Fr e d eri c k S o u z a L eit e a n d Y u Xi a o

[ 3] J. C h a u h a n, J. R aj as e g ar a n, S. S e n e vir at n e, A. Misr a, A. S e n e vir at n e, a n d Y. L e e.2 0 1 8. P erf or m a n c e C h ar a ct eri z ati o n of D e e p L e ar ni n g M o d els f or Br e at hi n g-b as e d A ut h e nti c ati o n o n R es o ur c e- C o nstr ai n e d D e vi c es. 2 0 1 8 A C M I M W U T 2, 4,Arti cl e 1 5 8 ( D e c. 2 0 1 8), 2 4 p a g es.

[ 4] R. C h a v arri a g a, H. S a g h a, A. C al atr o ni, S. T. Di g u m arti, G. Tr öst er, J. d el R. Mill á n,a n d D. R o g g e n. 2 0 1 3. T h e O p p ort u nit y c h all e n g e: A b e n c h m ar k d at a b as e f oro n- b o d y s e ns or- b as e d a cti vit y r e c o g niti o n. P att er n R e c o g niti o n L ett ers 3 4, 1 5( 2 0 1 3), 2 0 3 3 – 2 0 4 2.

[ 5] M. E d el a n d E. K ö p p e. 2 0 1 6. Bi n ari z e d- B L S T M- R N N b as e d H u m a n A cti vit yR e c o g niti o n. I n I PI N’ 1 6. 1 – 7.

[ 6] B. F a n g, X. Z e n g, a n d M. Z h a n g. 2 0 1 8. N est D N N: R es o ur c e- A w ar e M ulti- Te n a ntO n- D e vi c e D e e p L e ar ni n g f or C o nti n u o us M o bil e Visi o n. I n M o bi C o m ’ 1 8 ( N e wD el hi, I n di a). A C M, 1 1 5 – 1 2 7.

[ 7] Y. G u a n a n d T. Pl öt z. 2 0 1 7. E ns e m bl es of D e e p L S T M L e ar n ers f or A cti vit yR e c o g niti o n Usi n g We ar a bl es. Pr o c. A C M I nt er act. M o b. We ar a bl e U bi q uit o usTe c h n ol. 1, 2, Arti cl e 1 1 (J u n e 2 0 1 7), 2 8 p a g es.

[ 8] N. D. L a n e, S. B h att a c h ar y a, A. M at h ur, C. F orli v esi, a n d F. K a ws ar. 2 0 1 6. D X T K:E n a bli n g R es o ur c e- e ffi ci e nt D e e p L e ar ni n g o n M o bil e a n d E m b e d d e d D e vi c eswit h t h e D e e p X T o ol kit. I n M o bi C A S E’ 1 6 . 9 8 – 1 0 7.

[ 9] J. L o n g, W. S u n, Z. Y a n g, O. I. R a y m o n d, a n d B. Li. 2 0 1 9. D u al R esi d u al N et-w or k f or A c c ur at e H u m a n A cti vit y R e c o g niti o n. C o R R a bs/ 1 9 0 3. 0 5 3 5 9 ( 2 0 1 9).ar Xi v: 1 9 0 3. 0 5 3 5 9

[ 1 0] F. J. Or d ó ñ e z a n d D. R o g g e n. 2 0 1 6. D e e p C o n v ol uti o n al a n d L S T M R e c urr e ntN e ur al N et w or ks f or M ulti m o d al We ar a bl e A cti vit y R e c o g niti o n. S e ns ors 1 6, 1( 2 0 1 6).

[ 1 1] D. R a vi, C. W o n g, B. L o, a n d G. Y a n g. 2 0 1 6. D e e p l e ar ni n g f or h u m a n a cti vit yr e c o g niti o n: A r es o ur c e e ffi ci e nt i m pl e m e nt ati o n o n l o w- p o w er d e vi c es. I n 2 0 1 6I E E E B S N. 7 1 – 7 6.

[ 1 2] A. R eiss a n d D. Stri c k er. 2 0 1 2. I ntr o d u ci n g a N e w B e n c h m ar k e d D at as et f orA cti vit y M o nit ori n g. I n I S W C’ 1 6. 1 0 8 – 1 0 9.

[ 1 3] C. X u, D. C h ai, J. H e, X. Z h a n g, a n d S. D u a n. 2 0 1 9. I n n o H A R: A D e e p N e ur alN et w or k f or C o m pl e x H u m a n A cti vit y R e c o g niti o n. I E E E Acc ess 7 ( 2 0 1 9), 9 8 9 3 –9 9 0 2.

[ 1 4] S. Y a o, Y. Z h a o, H. S h a o, S. Li u, D. Li u, L. S u, a n d T. A b d el z a h er. 2 0 1 8. F ast D e e pI o T:T o w ar ds U n d erst a n di n g a n d O pti mi zi n g N e ur al N et w or k E x e c uti o n Ti m e o nM o bil e a n d E m b e d d e d D e vi c es. I n S e ns ys ’ 1 8 ( S h e n z h e n, C hi n a). A C M, N e w Y or k,N Y, U S A, 2 7 8 – 2 9 1.