Fall detection using signals from real-world fall events

Innovation for Tomorrow: What the future holds

1Robert-Bosch Hospital Stuttgart, Germany 2University of Bologna, Italy 3Norwegian University of Science and Technology, Trondheim, Norway

Jochen Klenk1, Luca Palmerini2, Alan Bourke3, Lars Schwickert1, Lorenzo Chiari2, Clemens Becker1

Fall consequences – long lying

■ Less than 20% of all falls are observed by others

■ >10% long lying situations

■ Association between lying duration and consequences:

28% mortality

62% hospital admissions

62% of survivors were unable to live independently

Gurley RJ et al. Persons Found in Their Homes Helpless or Dead. New England Journal of Medicine. 1996 Jun 27;334(26):1710–6.

Automatic fall detection

■ Focus on body-worn sensor technology including accelerometers and gyroscopes:

Cheap, small & light weight

Mobile (indoor & outdoor)

Good patient compliance

Performance of published fall detection algorithms

Bagalà F et al. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE. 2012;7(5):e37062.

Common features used for fall detection

■ Impact detection

Acceleration sum vector magnitude (total, dynamic)

Maximal vertical velocity

Maximum jerk

■ Change of posture

Critical trunk inclination

Angular velocity

Simulated vs. real-world falls

* P < 0.05 (exact Wilcoxon test)

Klenk J et al. Comparison of acceleration signals of simulated and real-world backward falls.

Med Eng Phys. April 2011;33(3):368–73.

The FARSEEING meta-database

Subject

characteristics

- age

- gender

- disease

- function

- …

Technical

characteristics

- type of sensor

- sample rate

- sensor site

- duration

- …

Fall

characteristics

- date & time

- fall direction

- verification

- outcome

- …

Real-world fall meta-database

Fall signals

- accelerometer

- gyroscope

- magnetometer

Klenk J, Chiari L, Helbostad JL, Zijlstra W, Aminian K, et al. Development of a standard fall data

format for signals from body-worn sensors. Z Gerontol Geriat. 2013 Dec 1;46(8):720–6.

n = 208

Verified fall signals

Real-world fall example

vertical

medio-lateral

anterior-posterior

Device: Samsung Galaxy S3

Sample rate: 100 Hz

Range: ±20 m/s²

While pushing the door opener

falling backwards

Self-recovery

Kinematic parameters of real-world falls

Bourke AK, Klenk J, Schwickert L, et al. Temporal and kinematic variables for real-world falls harvested from lumbar sensors

in the elderly population. IEEE EMBC 2015.

Pattern recognition – the wavelet approach

Palmerini L, Bagala F, Zanetti A, Klenk J, Becker C, Capello A. A wavelet-based approach to fall detection. Sensors 2015

Common patterns – the wavelet approach

AUC = 0.98 (0.96-0.99)

95% sensitivity, 97% specificity

50% sensitivity, 99.99% specificity

Palmerini L et al. Unpublished 2016

Orientation estimation

“When washing the hair, towel slipped over the forehead and covered the eyes. Due to loss of vision, subject fell backwards.”

Madgwick SOH, et al. Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR). 2011. p. 1–7.

Example of unique fall patterns

“Lost balance while bending down to pick up an object from the floor, then falling forward to the right side.”

Future directions

■ Combining different fall detection approaches

■ Setting- and disease-specific algorithms

■ Self-learning approaches (normal patterns)

■ Detection of recovery movements

Conclusion

■ Reliable fall detection is a promising technology to support independent

living

■ Simulated data is not sufficient for algorithm development and testing

■ Real-world data is available for analysis

■ Real-world fall signals improve performance of fall detection algorithms