A Modular Design for Distributed Measurement of Human-Robot Interaction Forces in Wearable Devices

Human-Robot Joint Misalignment, Physical Interaction, and Gait Kinematic Assessment in Ankle-Foot Orthoses

Lower limb exoskeletons and orthoses have been increasingly used to assist the user during gait rehabilitation through torque transmission and motor stability. However, the physical human-robot interface (HRi) has not been properly addressed. Current orthoses lead to spurious forces at the HRi that cause adverse effects and high abandonment rates. This study aims to assess and compare, in a holistic approach, human-robot joint misalignment and gait kinematics in three fixation designs of ankle-foot orthoses (AFOs). These are AFOs with a frontal shin guard (F-AFO), lateral shin guard (L-AFO), and the ankle modulus of the H2 exoskeleton (H2-AFO). An experimental protocol was implemented to assess misalignment, fixation displacement, pressure interactions, user-perceived comfort, and gait kinematics during walking with the three AFOs. The F-AFO showed reduced vertical misalignment (peak of 1.37 ± 0.90 cm, p-value < 0.05), interactions (median pressures of 0.39-3.12 kPa), and higher user-perceived comfort (p-value < 0.05) when compared to H2-AFO (peak misalignment of 2.95 ± 0.64 and pressures ranging from 3.19 to 19.78 kPa). F-AFO also improves the L-AFO in pressure (median pressures ranging from 8.64 to 10.83 kPa) and comfort (p-value < 0.05). All AFOs significantly modified hip joint angle regarding control gait (p-value < 0.01), while the H2-AFO also affected knee joint angle (p-value < 0.01) and gait spatiotemporal parameters (p-value < 0.05). Overall, findings indicate that an AFO with a frontal shin guard and a sports shoe is effective at reducing misalignment and pressure at the HRI, increasing comfort with slight changes in gait kinematics.

Assessment of Socket Pressure during Walking in Rapid Fit Prosthetic Sockets

(1) Background: A sustainable casting system that combines the use of a polystyrene bag, a prosthetic liner and a vacuum system was developed to reduce fabrication time while maintaining comfort for the trans-tibial prosthesis user. (2) Methods: Eight prosthetists (28.7 ± 8.25 years old) fit ten trans-tibial prosthesis wearers (46 ± 12.4 years old) with two types of total surface bearing (TSB) prostheses; a polystyrene bead (PS) prosthesis and a plaster of paris (POP) prosthesis. Duration of casting and combined mean peak pressure was measured at six locations on the residual limb using Force Sensing Resistors (FSR). A pressure uniformity score (%) was determined. Socket Comfort Scale (SCS) was also measured. (3) Results: Duration of casting for the POP method was 64.8 ± 9.53 min and 7.8 ± 2 min for the PS method, (p = 0.006). Pressure uniformity in the POP prosthesis was 79.3 ± 6.54 and 81.7 ± 5.83 in the PS prosthesis (p = 0.027). SCS in both prosthesis types were equivalent. (4) Conclusion: A rapid fit PS prosthesis was developed, with significantly shorter duration than the traditional POP method. Socket pressure uniformity was confirmed and improved in the PS method. Socket comfort was equal between the two prothesis types.

Characterization and Evaluation of Human-Exoskeleton Interaction Dynamics: A Review

Exoskeletons and exosuits have witnessed unprecedented growth in recent years, especially in the medical and industrial sectors. In order to be successfully integrated into the current society, these devices must comply with several commercialization rules and safety standards. Due to their intrinsic coupling with human limbs, one of the main challenges is to test and prove the quality of physical interaction with humans. However, the study of physical human-exoskeleton interactions (pHEI) has been poorly addressed in the literature. Understanding and identifying the technological ways to assess pHEI is necessary for the future acceptance and large-scale use of these devices. The harmonization of these evaluation processes represents a key factor in building a still missing accepted framework to inform human-device contact safety. In this review, we identify, analyze, and discuss the metrics, testing procedures, and measurement devices used to assess pHEI in the last ten years. Furthermore, we discuss the role of pHEI in safety contact evaluation. We found a very heterogeneous panorama in terms of sensors and testing methods, which are still far from considering realistic conditions and use-cases. We identified the main gaps and drawbacks of current approaches, pointing towards a number of promising research directions. This review aspires to help the wearable robotics community find agreements on interaction quality and safety assessment testing procedures.

Application of artificial intelligence in wearable devices: Opportunities and challenges

BACKGROUND AND OBJECTIVES

Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data.

METHODS

Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives.

RESULTS

Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays.

CONCLUSION

The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.