Efficient Multiaxial Shoulder-Motion Tracking Based on Flexible Resistive Sensors Applied to Exosuits

Multi-modal deformation and temperature sensing for context-sensitive machines

Owing to the remarkable properties of the somatosensory system, human skin compactly perceives myriad forms of physical stimuli with high precision. Machines, conversely, are often equipped with sensory suites constituted of dozens of unique sensors, each made for detecting limited stimuli. Emerging high degree-of-freedom human-robot interfaces and soft robot applications are delimited by the lack of simple, cohesive, and information-dense sensing technologies. Stepping toward biological levels of proprioception, we present a sensing technology capable of decoding omnidirectional bending, compression, stretch, binary changes in temperature, and combinations thereof. This multi-modal deformation and temperature sensor harnesses chromaticity and intensity of light as it travels through patterned elastomer doped with functional dyes. Deformations and temperature shifts augment the light chromaticity and intensity, resulting in a one-to-one mapping between stimulus modes that are sequentially combined and the sensor output. We study the working principle of the sensor via a comprehensive opto-thermo-mechanical assay, and find that the information density provided by a single sensing element permits deciphering rich and diverse human-robot and robot-environmental interactions.

Clinical applications of smart wearable sensors

Smart wearable sensors are electronic devices worn on the body that collect, process, and transmit various physiological data. Compared to traditional devices, their advantages in terms of portability and comfort have made them increasingly important in the medical field. This review takes a unique clinical physician's standpoint, diverging from conventional sensor-type-based classifications, and provides a comprehensive overview of the diverse clinical applications of wearable sensors in recent years. In this review, we categorize these applications according to different diseases, encompassing skin diseases and injuries, cardiovascular diseases, abnormal human motion, as well as endocrine and metabolic disorders. Additionally, we discuss the challenges and perspectives hindering the development of sensors for clinical use, emphasizing the critical need for interdisciplinary collaboration between medical and engineering professionals. Overall, this review would serve as an important reference for the future direction of sensor devices in clinical use.

Upper limb soft robotic wearable devices: a systematic review

INTRODUCTION

Soft robotic wearable devices, referred to as exosuits, can be a valid alternative to rigid exoskeletons when it comes to daily upper limb support. Indeed, their inherent flexibility improves comfort, usability, and portability while not constraining the user's natural degrees of freedom. This review is meant to guide the reader in understanding the current approaches across all design and production steps that might be exploited when developing an upper limb robotic exosuit.

METHODS

The literature research regarding such devices was conducted in PubMed, Scopus, and Web of Science. The investigated features are the intended scenario, type of actuation, supported degrees of freedom, low-level control, high-level control with a focus on intention detection, technology readiness level, and type of experiments conducted to evaluate the device.

RESULTS

A total of 105 articles were collected, describing 69 different devices. Devices were grouped according to their actuation type. More than 80% of devices are meant either for rehabilitation, assistance, or both. The most exploited actuation types are pneumatic (52%) and DC motors with cable transmission (29%). Most devices actuate 1 (56%) or 2 (28%) degrees of freedom, and the most targeted joints are the elbow and the shoulder. Intention detection strategies are implemented in 33% of the suits and include the use of switches and buttons, IMUs, stretch and bending sensors, EMG and EEG measurements. Most devices (75%) score a technology readiness level of 4 or 5.

CONCLUSION

Although few devices can be considered ready to reach the market, exosuits show very high potential for the assistance of daily activities. Clinical trials exploiting shared evaluation metrics are needed to assess the effectiveness of upper limb exosuits on target users.

Damian D. Hyperelastic Membrane Actuators: Analysis of Toroidal and Helical Multifunctional Configurations

Technologies that provide mechanical assistance are required in the medical field, such as implants that regenerate tissue through elongation and stimulation. One of the challenges is to develop actuators that combine the benefits of high axial extension at low pressures, modularity, multifunction, and load-bearing capabilities into one design while maintaining their shape and softness. Overcoming such a challenge will provide implants with enhanced capacity for mechanical assistance to induce tissue regeneration. We introduce two novel actuators (M2H) built of stacked Hyperelastic Ballooning Membrane Actuators (HBMAs) that can be realized using helical and toroidal configurations. By restraining the HBMA expansion deterministically using a semisoft exoskeleton, the actuators are endowed with axial extension and radial expansion capabilities. These actuators are thus built of modules that can be configured to different therapeutical needs and multifunctionality, to provide anatomically congruent stimulation. We present the design, fabrication, testing, and numerical and experimental validation of the M2H-HBMAs. They can axially extend up to 41% and 32% in their helical and toroidal configurations at input pressures as low as 26 and 24 kPa, respectively. If the axial extension module is used separately, its extension capacity reaches >170%. The M2H-HBMAs can perform independent and simultaneous expansion and extension motions with negligible intraluminal deformation as well as stand at least 1 kg of axial force without collapsing. The M2H-HBMAs overcome the limitations of hyperexpanding machines that show low resistance to load. We envisage M2H-HBMAs as promising tools to perform tissue regeneration procedures.

A Self-Powered Portable Flexible Sensor of Monitoring Speed Skating Techniques

With the development of 5G technology, contemporary technologies such as Internet of Things (IoT) and Big Data analyses have been widely applied to the sport industry. This paper focuses on the design of a portable, self-powered, flexible sensor, which does not require an external power supply. The sensor is capable of monitoring speed skating techniques, thereby helping professional athletes to enhance their performance. This sensor mainly consists of Polyvinylidene Fluoride (PVDF) with polarization after a silvering electrode and a flexible polyester substrate. Flexible sensors are attached to the push-off joint part of speed skaters and the ice skate blade. During motion, it produces different piezoelectricity signals depending on the states of motion. The monitoring and analyzing of the real-time sensor signals will adjust the athlete's skating angle, frequency, and push-off techniques, thus improving user training and enhancing performance. Moreover, the production of piezoelectric signals can charge the capacitor, provide power for small electronic equipment (e.g., wireless device), and extend the applications of wearable flexible sensors to the Big Data and IoT technologies in the sport industry.

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

Efficient Upper Limb Position Estimation Based on Angular Displacement Sensors for Wearable Devices

Motion tracking techniques have been extensively studied in recent years. However, capturing movements of the upper limbs is a challenging task. This document presents the estimation of arm orientation and elbow and wrist position using wearable flexible sensors (WFSs). A study was developed to obtain the highest range of motion (ROM) of the shoulder with as few sensors as possible, and a method for estimating arm length and a calibration procedure was proposed. Performance was verified by comparing measurement of the shoulder joint angles obtained from commercial two-axis soft angular displacement sensors (sADS) from Bend Labs and from the ground truth system (GTS) OptiTrack. The global root-mean-square error (RMSE) for the shoulder angle is 2.93 degrees and 37.5 mm for the position estimation of the wrist in cyclical movements; this measure of RMSE was improved to 13.6 mm by implementing a gesture classifier.