Understanding the Human Motor Controlfor User-Centered Design of Custom

Wearable Systems: Case Studiesin Sports, Industry, Rehabilitation

Teodorico Caporaso1(B), Stanislao Grazioso1, Dario Panariello1,2,Giuseppe Di Gironimo1, and Antonio Lanzotti1

1 Fraunhofer Joint Lab IDEAS, Dipartimento di Ingegneria Industriale,Universita degli Studi di Napoli Federico II, P.le Tecchio 80, 80125 Napoli, Italy

teodorico.caporaso@unina.it2 Dipartimento di Ingegneria Gestionale, dell’Informazione e della Produzione,

Universita degli Studi di Bergamo, Viale Marconi 5, 24044 Dalmine, BG, Italy

Abstract. This paper shows how studies on the biomechanics and neu-roscience of human movements might be used for the design of wearablesystems customized for humans. Such design is driven by key biomechan-ical and neuromuscular parameters extracted from accurate measure-ments made on the human body motion, as well as by subjective datacollected from the end-users of the products through questionnaires. Wepresent three case studies developed at ERGOS Lab: a wearable systemfor sports performance analysis; a synergy-based approach for industrialwearable robots; a soft wearable robotic glove for hand rehabilitation.

Keywords: Design methods · Biomechanics ·Neuromuscular activity · Wearable technology

1 Introduction

Wearable systems are becoming widespread in many different fields, as sports andfitness, healthcare and wellness, safety and prevention in workplaces. They canbe used for recording of human-related performance parameters for monitoringapplications, or they can be used for aiding people in performing movements. Inthe first case, we refer to wearable devices [1], while in the second case, we referto wearable robots [2].

The effective development of wearable systems require an extensive knowl-edge on the human behaviors. This knowledge might be acquired by understand-ing the human motor control, i.e. the process that involves brain, muscles, limbsand external objects to regulate human movements [3]. As the human motor con-trol involves a cooperative interaction between the central nervous system and

c© Springer Nature Switzerland AG 2020C. Rizzi et al. (Eds.): ADM 2019, LNME, pp. 753–764, 2020.https://doi.org/10.1007/978-3-030-31154-4_64

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the musculoskeletal system, understanding this process requires a deep compre-hension on the neuromuscular and biomechanical activities which generate thehuman motion.

In sport, understanding the human motor control could allow the develop-ment of wearable devices that can monitor and therefore maximize athletes’performance. In industrial workplaces, understanding the human motor con-trol can bring to the development of custom wearable systems which guide theworkers towards ergonomically correct postures [4], or can reduce the opera-tor’s workload during the activities [5]. Finally, in rehabilitation medicine, whereare present deficiency in the execution of normal movements, understandingthe human motor control is fundamental to restore the functions of the motorsystem.

As these systems have to be worn by humans, it is also important to extrap-olate information from the end-users of the products, in particular related totheir internal sensations and feeling with the technology. In this respect, a sub-jective way of analysis consists of submitting questionnaires to capture the users’emotions; this is of great importance for the development of human-centereddevices [6,7]. Many design methods exist to obtain such kind of information;however, one of the most well-known approaches is represented by the Kanseiengineering methodology. This approach allows capturing the internal sensationsfrom the end-users and the identification of product quality elements satisfyingboth functional and emotional needs [8].

In this work we show how neuromuscular and biomechanical studies mightgenerate human-related key design parameters for wearable systems. To valorizethis strategy, we present three case studies conducted at ERGOS Lab, CeSMA(Centre for Advanced Metrology Services, University of Naples Federico II, Italy)in different domains of applications: sports, industry, rehabilitation. Then, wepresent an extensive discussion which can be used to guide further works in thiscontext and we report some conclusions.

2 Method

In this work we show how key input parameters obtained from the human motorcontrol system can be used to design of customized wearable systems for humans.In order to understand the basics of human motor control, biomechanics andneuroscience of human movements play a relevant role. The biomechanics ofhuman motion studies the musculoskeletal system for the assessment of move-ments. Nevertheless, for a full understanding of the human motor control, it isimportant to take into consideration how the neural system acts in generatingthe movements: this is done through the study of neuroscience applied to humanmotion. The integrated approach for the assessment of human motor control isshown in Fig. 1. From one hand, this process outputs biomechanical parameterswhich can be implemented on wearable devices for monitoring of human perfor-mances. From the other hand, it can output some information as range of motion

Understanding the Human Motor Control for Design of Wearable Systems 755

Fig. 1. Schematic diagram of the assessment of human motor control. On the left themain steps of biomechanics approach and on the right neuroscience ones.

of limbs and neuromuscular synergies of movement, which can be the basis forthe development of wearable robots (i.e. exoskeletons) able to work closely tohumans.

2.1 Biomechanics of Human Motion

The human movements are assessed through a biomechanics process involv-ing several steps. First, two preliminary steps are required: (1) measurementof human movements (i.e. kinematic parameters acquired by an inertial sensor);(2) description of human movements (i.e. different graphical way to visualize themeasurements). Then, the combination of measurements and description datainto specific biomechanical models allows doing analysis of the movements [9].The construction of these models is based on the combination of physical and bio-logical principles. Typical models of the human skeletons used in the biomechan-ics community involve rigid-body segments connected by frictionless joints [10].

The use of these models with biomechanical experimental measurements (i.e.trajectories of specific land-markers, ground reactions forces and antropometricalcharacteristics), allows to assess the whole-body motion (i.e. during a walk).At the end of this analysis phase, a large number of information related to themovement can be available. The last stage for the assessment of the biomechanicsof human motion is the interpretation. This stage aims at individuating specifickey parameters able to summarize the most important information related tothe human motion. These key parameters are interpreted in a custom way basedon the main characteristics of the end-users.

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Selection of human movement

Human biosignal

acquisition

Design ofwearable system

Extraction ofkey

parameters

Human biosignal

processing

Fig. 2. A generic flow chart for bio-inspired design of customized wearable systems.

2.2 Neuroscience of Human Motion

The neuroscience of human motion allows to understand the role of humanneural system for generating the motor actions. As done with biomechanics,we need to find proper measurement data related to the neural control sys-tem: with this respect, electromyography (EMG) allows recording the electricalactivity produced by muscles as they are neurologically activated. Therefore,measurements are done using EMG sensors placed on the human body in cor-respondence of the muscles involved in the movements. After the measurementprocess, we need to describe the data. Specific models allows to analyze the pro-cesses underpinning this neuro-muscular activity. This activity is highly complex;however, reduced-order models exists which condense the information related tothe neuro-muscolar activation process, as the concept of neuromuscular syner-gies. In the context of motor control, synergies are defined as groups of musclesjointly activated by a single central control signal [11]. Through the sequencingand superposition of a limited number of synergies, a large variation of complexmovements can be achieved [12]. This concept can be implemented on roboticprostheses and exoskeletons, for simplifying the control of a multi degrees-of-freedom system through emulating only few synergies.

3 Examples of Applications

In this section we show with three practical examples how studies on the humanmotor control can be used for design of custom wearable devices. The three casestudies refer to three different fields of applications: sports, industry, rehabilita-tion. Their development follows the design methodology illustrated in Fig. 2.

3.1 Sports

Biomechanics of human motion is used in the case study #1 for the develop-ment of a wearable system for sports performance analyses customized for eliteathletes.

Case Study #1: Inertial Assistant Referee and Trainer for Race-Walking

Problem Statement: Currently, the most common infringement in race walkingis the loss of ground contact (LOGC), which is evaluated during the race by thehuman eyes of judges (it lasts few hundredths of seconds). Therefore, a wearable

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system (composed by a measurement unit placed on the athletes’ body and amanagement unit for judges) able to automatically assess the infringements inrace-walking is highly desirable. On the other side, maximizing the performance(which, in this discipline, is strictly related to infringements) is also required.Starting from these motivations, we develop a system able to simultaneouslydetect the infringements and assess the performances.

Methodological Approach: The measurement unit is composed by an inertialsystem placed at the end of the athlete vertebral column (L5–S1). This placementwas chosen according to two input data: (i) estimation of the 3D body centerof mass during human locomotion [13,14]; (ii) users sensation extracted fromspecific questionnaires according to a Kansei engineering approach [15]. Themeasurement data are translated in biomechanical parameters customized forelite athletes for the assessment of infringement (i.e. estimation and classificationof LOGC) and performance (i.e. step cadence, step length and smoothness) [16].These parameters are plotted simultaneously on a radar chart, and shown by amobile app for smartphone [17]. The design process of this wearable device isshown in Fig. 3.

Results: The system estimates the LOGC with an accuracy of more than80% [18]. The management unit composed of a mobile app is considered intuitivefrom coaches (it allows to easily understand the strong and critical point of theathletes technique [16]) and judges (it helps in the assessment of sequence ofLOGC [18]).

3.2 Industry

In industry, one of the most recent trend is related to the humanization of facto-ries1, which aims at developing bionic workplaces2 where humans and machinescan work together in a symbiotic way. In this respect, biomechanics and neu-romuscular studies on the humans can play a relevant role for the design ofassistive devices, as industrial exoskeletons [19,20], which can potentially reducethe workers’ physical overloading.

Case Study #2: Synergy-Based Method for the Development of SoftWearable Industrial Robots

Problem Statement: The current industrial exoskeletons present the followingproblems: (1) they are bulky; (2) they reduce the operative workspace avail-able for workers and they can damage the surrounding objects; (3) they do notinherently guarantee a safe human-robot interaction; (4) their motion can beperceived as non-natural for the humans. The development of more effective

1 https://www.robotics.org/.2 https://www.festo.com/.

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Race walking Extraction of key parameters

δ

μ

ρ

γ α

0.8

0.6

0.4

0.2

0

β1

Signal acquisition and processing

Time [s]

0.2 0.25 0.3 0.35 0.4In

ertia

l CoM

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eler

atio

n (m

/s2)

-15

-10

-5

0

5

10

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Negative PeakAcceleration

E

TOE HSE

LOGC

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Mobile app

Judge Mode

CoachMode

Fig. 3. Design of a wearable system for race walking. In the second rectangle, the cor-relation between the CoM accelerations and gait temporal events (TOE: toe-off event;HSE: heel strike event) for assessing the LOGC (using the threshold value E). In thethird rectangle performance (μ, ρ, γ), infringement (α, δ) and overall (β) parameters.

assistive devices require a new radical design philosophy [21]. Therefore, we pro-pose a strategy for design and control of industrial wearable robots which isbased, from one side, on the extraction of neuromuscular synergies during theexecution of industrial tasks, from the other side, on the use of soft roboticstechnology [22–24].

Methodological Approach: First, four industrial tasks of interest are selected.These tasks are those for which the exoskeleton has to provide assistance. Then,EMG sensors are placed on the worker’s body according to a specific proto-col, in order to capture the muscular activity. Three repetitions are performedfor the same task, for a total of twelve experiments. From the EMG data, themuscle activation curves are reconstructed. Then, from these, the muscle syn-ergies are extracted according to the non-negative matrix factorization (NMF)algorithm [25].

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Selection ofindustrial tasks

EMG signal acquisition

Reconstruction of muscle activation

Extraction ofmuscle synergies

Design of softartificial muscle

Fig. 4. The synergy – based approach for soft wearable robot. In the block “EMGsignal acquisition” we have the following muscles: M1, upper trapezium; M2, anteriordeltoid; M3, medial deltoid; M4, rear deltoid; M5, biceps brachii; M6, long head of thetriceps.

Results: For the considered industrial tasks, the neuromuscular study outputstwo major results: (1) the first synergy account for >98% of the total muscle acti-vation [25], and therefore it is sufficient for reconstructing the overall movements;(2) the most activated muscle is the anterior deltoid. These results suggest thatone artificial muscle (i.e. a soft actuator [26]) which replicates the first synergyand is placed in correspondence of the anterior deltoid muscle might reduce thephysical overloading on the workers. Indeed, soft actuators can be designed towork as a biological muscle; furthermore, as they are pneumatically actuated andcomposed of soft materials, they are intrinsically safe for workers. The overalldesign process of soft wearable industrial robots is illustrated in Fig. 4.

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3.3 Rehabilitation

In the recent year, there is a growing interest in using robotics technology tosupport physiatry. Rehabilitation robots must have the following features: safety,effectiveness, simplicity of use (for both patients and therapists). In the following,pneumatic actuation and soft materials are used together for the developmentof an intrinsically safe robotic glove [27] used for rehabilitation exercises.

Case Study #3: Design and Fabrication of a Soft Robotic Glove forHand Rehabilitation

Problem Statement: Existing robotics solutions for hand rehabilitation are lim-ited to medical centers, where the patients are under the supervision of phys-iotherapists. This happens because the existing solutions are usually heavy andthey could be invasive for the patients. The widespread of soft robotic technol-ogy offers an alternative way for the development of systems which can interactwith patients in a safer way [28]. Thus, a soft rehabilitation robot can be usedalso for home therapy, as it can be worn and actuated by the patient in a safeway. With this in mind, we develop a soft wearable robotic glove3 which enclosesthe hand and allows a whole-body manipulation of different objects.

Methodological Approach: First, power grasping of different objects is selected asrehabilitation exercise. In order to develop a system which can enclose the handand allows the grasping of different objects, we use soft bending actuators foraugment the natural movements of each finger. These actuators are composed bytwo materials with different stiffness and they are designed such that they bendwhen the air flows in. Therefore, they can guide a finger towards the graspingpose. The typical range of motion of an healthy hand is used to compute thedesired bending angle; indeed, anthropometric data of the natural fingers areused to size the length of each actuator, since they are custom fabricated. Amapping between bending angle and the input pressure is derived for actuationpurposes. The robotic glove is safe for humans as it is composed of a soft material(elastomer) and it is actuated by a pneumatic source. The design process ofthe soft robotic glove is illustrated in Fig. 5. In order to test the capabilitiesof the system, a volunteer performed ten grasping experiments for each of thefollowing objects: tennis ball, hard-disk case, plastic bottle, tape (for a total of40 experiments).

Results: The grasping experiments involved three phases: (i) getting close tothe object; (ii) grasping the object; (iii) keeping the object between fingers for5 s. The experiments are considered to be successful if all the three conditionsare successful. For the 40 experiments, we achieve a successful grasping in the77.5% of the cases. We actuate the robotic glove in the same way for all theexperiments: this underline how the system is robust with respect to differentobjects.3 https://softroboticstoolkit.com/.

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Selection of rehabilitation

exercises

Hand range of motion

Fabrication process

Design of soft robotic glove Actuation mechanism

270° of curvature

ΔP

= 0

ΔP

> 0

Air inlet

strainlimitinglayer

strainlimitinglayer

radial

rubber layerencapsulatingreinforcementshalf round

rodrubberbody

Fig. 5. Design of a soft robotic glove for hand rehabilitation

4 Discussion

The case studies presented in Sect. 3 are used to give practical examples ofthe more general design methodology shown in Fig. 2. This methodology haveincluded five main steps: (i) selection of human movements, as industrial or reha-bilitation tasks or sport gestures; (ii) acquisition of biosignals (biomechanical orneuromuscular measurements); (iii) processing of biosignals (through the defini-tion of specific models); (iv) extraction of key biomechanical or neuromuscularparameters; (v) definition of design factors related to the previous key param-eters and, finally, design of the system. A further improvement of the strategycould be including, in the design process, a 3D body scanning step useful fordesign of tailored assistive devices (in this case, only exoskeletons) by takinginto consideration the real three-dimensional shapes of the subject [29–31]. Inthis work, the step in (iv) has been implemented and used in three different ways:(a) performance indicators used for monitoring of activities (Sect. 3.1); (b) neuro-muscular indicators (i.e. synergies) used for defining the architecture and controlstrategy of a wearable robots (Sect. 3.2); (c) human-centered parameters used todesign a wearable system (Sect. 3.3).

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The proposed approach can be used to improve the design phases of manyproducts, as it allows to obtain more customized, minimally invasive and effec-tive systems devoted for humans. As a rule, a deep knowledge on the humanmotor control allows, from one hand, the development of effective devices formonitoring of human activities, from the other hand, the fabrication of wearablerobots which behave and think similarly to humans. In the context of wearabledevices, for example, this approach underlines the importance of deriving biome-chanical indices customized for a particular category of end-user; moreover, thesuch developed indices result easy to be interpreted. As an illustrative exam-ple, authors in [32] have also proposed a system able to detect infringements inrace-walking (as the one proposed in Sect. 3.1), but using two inertial sensors.However, their detection algorithms are not customized on the specific require-ments of race-walking end-users (athletes, coaches and judges). In the contextof wearable robots, neuromuscular studies could bring to the implementation ofrobot control algorithms closed to those generated by the central nervous systemof human beings. Indeed, from the technology point of view, we believe that softmaterials and pneumatic actuation could represent a breakthrough in wearablerobotics, as it allows to guarantee an intrinsic safety of the exoskeleton.

It is importance to notice that the wearable systems under investigation inthis work are products which are directly in contact with the humans for along period during the day (i.e. about 3 h/day for athletes, 8 h/day for workers,1–2 h/day for patients). Therefore, it is important to put a great effort in thedesign phase for the development of effective and, at the same time, user-friendlyproducts.

In addition, as these systems have to be worn by humans, it is importantto analyse the users’ emotional needs. For example, in the case study #1, thisaspect has been analysed through questionnaires developed according to theKansei engineering method.

A further advance in this direction could be the development of objectivetechniques for quantifying the internal feelings and sensations. From the physi-ological point of view, the emotional life is largely housed in the limbic system,which also directs intentional movements. Therefore, applied neuroscience tech-niques (aims at correlating the internal sensations with measurements of elec-troencephalography, electromyography, heart rate and galvanic skin sensitivity)could offer rules and methods to quantify the Kansei outputs [33].

5 Conclusions

In this work we have presented some recent activities carried out at ERGOS Labin the context of wearable systems. These activities show how studies on thehuman motor control might be useful for the design of user-centered systems, indifferent domains. These studies could be used in the future for improving manyof existing and recognized design methods, as they can provide quantitativedata related to humans behavior to be included in the overall design process ofwearable systems.

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