The objectives of the present study were (a) to determine the effects of a 12-week intervention using wearables promoted through physical education classes on physical activity, body composition, physical fitness and psychological well-being of overweight or obese adolescents; and (b) to analyze the differences in outcomes based on gender and baseline physical activity. Seventy-three overweight and obese adolescents (mean age: 13.44 ± 1.12 years) were randomly assigned to an experimental group (EG) or control group (CG). The EG used a physical activity wearable for 12 weeks. Both groups were assessed before and after the intervention. Regarding primary outcomes, the EG showed an increase in physical activity (p = 0.048) and reductions in body mass index (p = 0.007), fat mass (p < 0.001), and sum of 3 skinfolds (p = 0.002), with moderate-to-large effect sizes (η2 > 0.09). According to the secondary outcomes, improvements in physical fitness were limited, with increases observed only in abdominal muscular endurance, and these changes were also present in the CG. Subgroup analyses showed that females and adolescents with low baseline physical activity experienced greater benefits, particularly in fat-related variables (p < 0.001–0.037), with large effect sizes (η2 > 0.14). Additionally, adolescents with greater exposure to the wearable-based intervention showed more consistent improvements in fat-related outcomes (p < 0.001–0.032), with large effect sizes (η2 > 0.25). In conclusion, a wearable-based intervention promoted through physical education classes may contribute to meaningful improvements in body composition, particularly among females and previously inactive adolescents who are overweight or obese. However, effects on physical fitness and psychological well-being were limited, highlighting the importance of intervention design, adherence, and complementary motivational strategies.

Poor posture is a significant concern due to its detrimental effects on health and productivity. This article presents a collar-clipped device called PostureClip, designed to restrict users from sitting and working at a bent angle, by blacking out the screen and resuming on correcting posture, thereby promoting better posture. The device integrates sensors and feedback mechanisms to provide real-time posture feedback to users. To evaluate the effectiveness of PostureClip, a controlled experiment was conducted with participants (n = 165) who were working on a laptop/PC for over 6 hours per day. The participants were randomly assigned to both the intervention group (IG1, n = 54; IG2, n = 55), which used the collar-clipped device, and the control group (CG, n = 56), which did not use the device. IG1 did not get feedback, while IG2 got feedback from the device by notifying and further darkening the screen. The study was conducted in the office environment of the participants, for 4 weeks, and metrics such as posture angle, duration of bent angle, and user feedback were collected. Analysis revealed significant improvements in posture angle (p < .001) and a significant reduction in bent angle duration (p < .01) for the participants’ group using PostureClip with feedback and compared to the group without feedback and the control group (who were not intervened). The qualitative analysis of user feedback highlighted the device’s ease of use, effectiveness in providing timely feedback, and positive impact on participants’ awareness and habits regarding posture. These results indicate that PostureClip is an effective tool for promoting better posture during sedentary work.

Machine – human interaction systems have been proposed to improve motion learning efficiency. We developed a pneumatic-driven motion teaching system that provides feedback to the learner by simultaneously presenting visual and torque information. We achieved a lightweight, soft, and user-safety haptic system using a pneumatic artificial muscle (PAM). The PAM’s shrink force was estimated based on its characteristic model and the suit link system, and the suit generated external torque. However, accurate force control was challenging due to the time delay of the feedback control, the loosening of the soft suit, and modeling errors of the driving PAM caused by hysteresis. To improve the force control performance of the motion teaching suit, this article’s contributions are to develop a novel suit in which PAMs for drive and force estimation are connected in series and implement a 2-degree-of-freedom (DOF) force control system using force estimation values in this suit and to confirm the effectiveness of the proposed hardware and software. This article contains three topics: (a) the development of novel suit hardware, (b) force estimation using a sealed small PAM, and (c) a proposal of force control using a 2-DOF controller. The effect of loosening the soft suit is reduced in the novel-developed suit. A sealed small PAM with small deformation and little hysteresis is adopted for force estimation. The time delay in feedback control is decreased by adopting the proposed novel 2-DOF control. Finally, the proposed suit and its control system were evaluated in experiments and achieved the desired performance.

This study introduces a novel method for gait analysis using a single inertial measurement unit placed on the sacrum. This method is valid not only on level ground but also on incline and decline conditions. The method leverages the “crackle” function, the third derivative of the sacral resultant acceleration, to identify right and left gait events. This approach is particularly effective in capturing the initial peak in acceleration data during foot impact with the ground, often overlooked by other methods. The study aimed to demonstrate the method’s accuracy in identifying the right- and left-side impacts during level ground, incline, and decline runs across a range of speeds. Additionally, the algorithm was applied in outdoor running scenarios, where it performed very well, further validating its robustness and reliability. The results are compared with other existing methods to highlight the effectiveness of this approach.

This article presents a bioinspired pneumatic soft actuator designed to mimic the flexo-extension movement of the human finger, with a particular focus on stiffness modulation through granular jamming. Three-chamber geometries – honeycomb, rectangular, and half-round – were evaluated to optimize curvature performance, utilizing Mold Star 15 Slow elastomer for actuator fabrication. Granular jamming, both passive and active, was implemented within the inextensible layer using chia and quinoa grains to enhance stiffness modulation. Experimental results revealed that the honeycomb geometry most closely aligned with the natural index finger trajectory. Stiffness evaluations demonstrated a range of 0–0.47 N/mm/° for quinoa and 0–0.9 N/mm/° for chia. The actuator’s force output increased by 16% for quinoa and 71% for chia compared to the nonjammed configuration. This enhanced performance is particularly beneficial for applications such as hand rehabilitation, where adaptive stiffness and force modulation are critical. Granular jamming, especially with active chia, provided superior adaptability for tasks requiring variable stiffness and resistance, making it a promising candidate for wearable robotic applications in rehabilitation.

Cable-driven exosuits have the potential to support individuals with motor disabilities across the continuum of care. When supporting a limb with a cable, force sensors are often used to measure tension. However, force sensors add cost, complexity, and distal components. This paper presents a design and control approach to remove the force sensor from an upper limb cable-driven exosuit. A mechanical design for the exosuit was developed to maximize passive transparency. Then, a data-driven friction identification was conducted on a mannequin test bench to design a model-based tension controller. Seventeen healthy participants raised and lowered their right arms to evaluate tension tracking, movement quality, and muscular effort. Questionnaires on discomfort, physical exertion, and fatigue were collected. The proposed strategy allowed tracking the desired assistive torque with a root mean square error of 0.71 Nm (18%) at 50% gravity support. During the raising phase, the electromyography signals of the anterior deltoid, trapezius, and pectoralis major were reduced on average compared to the no-suit condition by 30, 38, and 38%, respectively. The posterior deltoid activity was increased by 32% during lowering. Position tracking was not significantly altered, whereas movement smoothness significantly decreased. This work demonstrates the feasibility and effectiveness of removing the force sensor from a cable-driven exosuit. A significant increase in discomfort in the lower neck and right shoulder indicated that the ergonomics of the suit could be improved. Overall this work paves the way toward simpler and more affordable exosuits.

This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = $ {2.447}^{\circ } $) and mean absolute error (MAE = $ {2.006}^{\circ } $), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ($ \le 12.4\% $ of the joint range of motion [ROM]) and strong correlation coefficients ($ {r}^2>0.89 $). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.

This is a proof-of-concept study to compare the effects of a 2-week program of “Remind-to-move” (RTM) treatment using closed-loop and open-loop wearables for hemiparetic upper extremity in patients with chronic stroke in the community. The RTM open-loop wearable device has been proven in our previous studies to be useful to address the learned nonuse phenomenon of the hemiparetic upper extremity. A closed-loop RTM wearable device, which emits reminding cues according to actual arm use, was developed in this study. A convenience sample of 16 participants with chronic unilateral stroke recruited in the community was engaged in repetitive upper extremity task-specific practice for 2 weeks while wearing either a closed-loop or an open-loop ambulatory RTM wearable device on their affected hand for 3 hrs a day. Evaluations were conducted at pre-/post-intervention and follow-up after 4 weeks using upper extremity motor performance behavioral measures, actual arm use questionnaire, and the kinematic data obtained from the device. Results showed that both open-loop and closed-loop training groups achieved significant gains in all measures at posttest and follow-up evaluations. The closed-loop group showed a more significant improvement in movement frequency, hand functions, and actual arm use than did the open-loop group. Our findings supported the use of closed-loop wearables, which showed greater effects in terms of promoting the hand use of the hemiparetic upper extremity than open-loop wearables among patients with chronic stroke.

Robotic exoskeletons offer the potential to train novel motor skill acquisition and thus aid physical rehabilitation. Our prior work demonstrated that individuals converge to certain kinematic coordinations as they learn a novel task. An upper-limb exoskeleton controller that constrains individuals to this known coordination was also shown to significantly improve straight-line reaching task performance. This paper studies the impact of variations of this controller on novel skill acquisition. We quantify learning under three variations of the intervention (each group with N = 10 participants) against a control group (N = 13). Our results show that introducing any constraint during learning can hinder the learning process, as this alters the task dynamics that lead to success. However, when presented with a personalized constraint, participants still learn. When presented with a task-specific constraint, rather than a personalized one, participants cannot overcome the differences in the training and target task, suggesting exoskeleton-based training interventions should be personalized. The changes in kinematic behaviors during learning further suggest that participants do not have a statistically consistent performance. While participants respond more to exoskeleton intervention, others may not respond in short training sessions, necessitating further analysis of how strong a response can be encouraged. Our findings emphasize the need for further study of the effects of exoskeleton intervention for motor training and the potential need for personalization.