Real-time measurement of head rotation, a primary human body movement, offers potential advantages in rehabilitating head or neck motor disorders, promoting seamless human–robot interaction, and tracking the lateral glance of children with autism spectrum disorder for effective intervention. However, existing options such as cameras capturing the entire face or skin-attached sensors have limitations concerning privacy, safety, and/or usability. This research introduces a novel method that employs a battery-free RFID tag-based wearable sensor for monitoring head orientation, as a substitute for the existing options like camera. By attaching a pair of passive RFID tags to the front of the head at a specific distance from each other, the signal strength of each tag within the pair differs based on the discrepancy in distance from the RFID reader caused by head rotation. Important parameters including distance between the tags, distance from the reader, and tag types, are investigated to suggest optimal sensor design. In tests involving random head rotations by 10 healthy adults, there was a significant correlation between the orientation of the head and gaze in the yaw direction and the differences in signal strength from the sensor pairs. The correlation coefficients ($ {r}^2 $) were satisfactory, at 0.88 for head and 0.83 for left eye pupil orientations. However, the sensor failed to estimate pitch rotations for head and gaze, due to the insufficient vertical spacing between the tags. No demographic factors appeared to influence the results.

Wearable pressure sensors with high sensitivity, fast response time, and low detection limit have great potential for blood pressure monitoring and early diagnosis of hypertension. This article introduces a piezoresistive pressure sensor based on carbon nanotubes (CNTs), polyaniline (PAni), and fabric (CNT/PAni/fabric) for health monitoring applications. This sensor is made by using two layers of linen fabric coated with CNT and PAni. These layers are placed on a polyester fabric substrate. One of the coated layers has a mesh structure, which increases the sensitivity of the sensor and lowers its detection limit. The CNT/PAni/fabric sensor has a high sensitivity of 2.035 kPa−1 at pressures from 0 to 0.2 kPa, a response time of 290 ms, and a detection limit of 1.5 Pa. These features make it suitable for measuring blood pressure. The results obtained by measuring blood pressure using the pulse transit time method on four people, compared with the values obtained using the digital sphygmomanometer, show a discrepancy ranging between 0.019% and 1.62%. Also, the average error and standard deviation for the sensor measurement in systolic and diastolic pressures are 0.56 ± 0.33 and 0.57 ± 0.46, respectively, which shows that measurement with this sensor can be an alternative to existing devices.

Assessment of occupational exoskeletons should ideally include longitudinal and multistage studies in real working scenarios to prove their effectiveness and sustainability in real in-field contexts and to help generalize the findings for specific scenarios. This work presents a comprehensive assessment methodology implemented as a multistage experimental campaign for rail industry workers using a back-support exoskeleton (StreamEXO). This work demonstrates that a sector/task-specific exoskeleton developed to address work task-specific requirements generates beneficial performance and user experience results. The experimental work in this paper involves collecting data from nine workers over multiple days of testing. During this testing, workers did not report hindrances to their work operations, with an acceptance rate of 86%. In addition, worker fatigue was reduced by 16.9% as measured through metabolic consumption, and 51% when assessed by perceived effort. This work supports the hypothesis that sector/task-specific exoskeletons when tailored to meet the needs of workers and the work tasks can produce demonstrable benefits in real industrial sectors.

We have developed an interactive system comprising a soft wearable robot hand and a wireless task board, facilitating the interaction between the hand and regular daily objects for task-oriented training in stroke rehabilitation. A ring-reinforced soft actuator (RSA) to accommodate different hand sizes and enable flexion and extension movements was introduced in this paper. Individually controlled finger actuators assist stroke patients during various grasping tasks. A wireless task board was developed to support the training, allowing for the placement of training objects and seamless interaction with the soft robotic hand. Evaluation with seven stroke subjects shows significant improvements in upper limb functions (FMA), hand-motor abilities (ARAT, BBT), and maximum grip strengths after 20 sessions of this task-oriented training. These improvements were observed to persist for at least 3 months post-training. The results demonstrate its potential to enhance stroke rehabilitation and promote hand-motor recovery. This lightweight, user-friendly interactive system facilitates frequent hand practice and easily integrates into regular rehabilitation therapy routines.

Sensorized insoles provide a tool for gait studies and health monitoring during daily life. For users to accept such insoles, they need to be comfortable and lightweight. Previous research has demonstrated that sensorized insoles can estimate ground reaction forces (GRFs). However, these insoles often assemble commercial components restricting design freedom and customization. Within this work, we incorporated four 3D-printed soft foam-like sensors to sensorize an insole. To test the insoles, we had nine participants walk on an instrumented treadmill. The four sensors behaved in line with the expected change in pressure distribution during the gait cycle. A subset of this data was used to identify personalized Hammerstein–Wiener (HW) models to estimate the 3D GRFs while the others were used for validation. In addition, the identified HW models showed the best estimation performance (on average root mean squared (RMS) error 9.3%, $ {R}^2 $=0.85 and mean absolute error (MAE) 7%) of the vertical, mediolateral, and anteroposterior GRFs, thereby showing that these sensors can estimate the resulting 3D force reasonably well. These results were comparable to or outperformed other works that used commercial force-sensing resistors with machine learning. Four participants participated in three trials over a week, which showed a decrease in estimation performance over time but stayed on average 11.35% RMS and 8.6% MAE after a week with the performance seeming consistent between days two and seven. These results show promise for using 3D-printed soft piezoresistive foam-like sensors with system identification regarding the viability for applications that require softness, lightweight, and customization such as wearable (force) sensors.

This paper investigates hand grasping, a fundamental activity in daily living, by examining the forces and postures involved in the lift-and-hold phases of grasping. We introduce a novel multi-sensory data glove, integrated with resistive flex sensors and capacitive force sensors, to measure the intricate dynamics of hand movement. The study engaged five subjects to capture a comprehensive dataset that includes contact forces at the fingertips and joint angles, furnishing a detailed portrayal of grasp mechanics. Focusing on grasp synergies, our analysis delved into the quantitative relationships between the correlated forces among the fingers. By manipulating one variable at a time—either the object or the subject—our cross-sectional approach yields rich insights into the nature of grasp forces and angles. The correlation coefficients for finger pairs presented median values ranging from 0.5 to nearly 0.9, indicating varying degrees of inter-finger coordination, with the thumb-index and index-middle pairs exhibiting particularly high synergy. The findings, depicted through spider charts and correlation coefficients, reveal significant patterns of cooperative finger behavior. These insights are crucial for the advancement of hand mechanics understanding and have profound implications for the development of assistive technologies and rehabilitation devices.

Advanced myoelectric prostheses feature multiple degrees of freedom (DoFs) and sophisticated control algorithms that interpret user motor intentions as commands. While enhancing their capability to assist users in a wide range of daily activities, these control solutions still pose challenges. Among them, the need for extensive learning periods and users’ limited control proficiency. To investigate the relationship between these challenges and the limited alignment of such methods with human motor control strategies, we examine motor learning processes in two different control maps testing a synergistic myoelectric system. In particular, this work employs a DoF-wise synergies control algorithm tested in both intuitive and non-intuitive control mappings. Intuitive mapping aligns body movements with control actions to replicate natural limb control, whereas non-intuitive mapping (or non-biomimetic) lacks a direct correlation between aspects, allowing one body movement to influence multiple DoFs. The latter offers increased design flexibility through redundancy, which can be especially advantageous for individuals with motor disabilities. The study evaluates the effectiveness and learning process of both control mappings with 10 able-bodied participants. The results revealed distinct patterns observed while testing the two maps. Furthermore, muscle synergies exhibited greater stability and distinction by the end of the experiment, indicative of varied learning processes.