Wearable technologies最新文献

Diastasis of rectus abdominis (DRA) is a common pathological condition in postpartum rehabilitation, but with limited treatment strategies. This study aimed to explore the effect of using a trunk-wearable neuromuscular electrical stimulation (NMES) device on postpartum women with moderate and severe DRA. A total of 84 postpartum women with an inter-rectus distance (IRD) of ≥3 cm were randomly assigned to two equal groups. The treatment group received a trunk-wearable NMES device and exercise therapy, whereas the control group received exercise only. We measured IRD and calculated treatment response proportion, improvement of trunk muscle strength, and low-back pain in both groups. Additionally, we evaluated quality of life (QoL) using the SF-36 questionnaire and Hernia-related Quality of Life Survey (HerQLes). Statistical analysis was performed using SAS 9.4. After 8-week treatment, the IRD of the umbilical (M3) sector showed a greater reduction in the treatment group (-10.6 [-17.9 to -3.3]%, p < 0.05). Patients in the treatment group had higher treatment response proportions (p = 0.0031 and p = 0.0010, W2 and W3, respectively). Additionally, the treatment group had higher Janda assessment scores and greater reduction in low-back pain (both p < 0.0001). QoL evaluation indicated greater improvements in the SF-36 questionnaire (pain and role-emotional scales,p < 0.05) and HerQLes (p < 0.0001) in the treatment group. The application of a trunk-wearable NMES device on DRA patients, accompanied by exercise therapy, significantly reduced IRD and increased the treatment response proportion. Moreover, we observed positive improvements in trunk muscle strength, low-back pain, and QoL.

In recent years, there has been growing interest regarding the impact of human movement quality on health. However, assessing movement quality outside of laboratories or clinics remains challenging. This study aimed to evaluate the capabilities of consumer-grade wearables to assess movement quality and to consider optimal sensor locations. Twenty-two participants wore Polar Verity Sense magnetic, angular rate, and gravity (MARG) sensors on their chest and both wrists, thighs, and ankles, while performing repeated bodyweight movements. The Madgwick sensor-fusion algorithm was utilized to obtain three-dimensional orientations. Concurrent validity, quantified using the root-mean-square-error (RMSE), was established against a Vicon optical motion capture system following time-synchronization and coordinate-system alignment. The chest sensors demonstrated the highest accuracies overall, with mean RMSE () less than 9.0° across all movements. In contrast, the wrist sensors varied considerably (). Ankle and thigh sensors yielded mixed results, with the ranging from 2.0° to 40.0°. Notably, yaw angles consistently demonstrated higher discrepancies overall, while pitch and roll were relatively more stable. This study highlights the potential of consumer-grade MARG sensors to increase the real-world applicability and accessibility of complex biomechanical models. It also accentuates the requirement for strategic sensor placement and refined calibration and postprocessing methods to ensure accuracy.

Musculoskeletal disorders remain a leading occupational health challenge in physically demanding sectors such as healthcare, social care, and industry. Exoskeletons - wearable devices designed to mitigate physical strain are increasingly explored as potential solutions; however, factors affecting their adoption in real-world settings remain underexplored. This novel scoping review systematically maps the existing evidence on the application of commercially available exoskeletons within real and simulated work environments, focusing on usage patterns, user experiences, and factors influencing implementation. Following the Joanna Briggs Institute methodology for scoping reviews, a systematic literature search was conducted across the Web of Science, Scopus, CINAHL, PsycINFO, and MEDLINE, with an initial search in May 2023 and an update in May 2024. Forty-nine papers met the inclusion criteria based on the Population, Concept, and Context (PCC) framework. Data were extracted using a standardized form and synthesized descriptively, thematically, and through content analysis. Results are presented in narrative, tabular, and conceptual map formats. Exoskeletons were used most frequently in industry (manufacturing) and perioperative care (healthcare). Although, the devices reduced muscle load during repetitive or static tasks, adoption was constrained by discomfort and fit challenges, thermal burden, and limited usability in dynamic settings. Thematic analysis revealed how user experiences were shaped by professional identity, task compatibility, organizational support, and social norms. A conceptual map synthesized sector-specific and cross-sectoral barriers and facilitators. This review highlights the need for inclusive, context-sensitive, and longitudinal research to support safe, acceptable, and effective exoskeleton adoption and implementation across diverse occupational environments.

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.

Gait analysis is a fundamental tool in biomechanics and rehabilitation, as it evaluates human movements' kinematic and kinetic behavior. For this reason, high-precision devices have been developed. However, these require controlled environments, which generates a deficiency in the capacity of studies related to gait analysis in outdoor and indoor scenarios. Therefore, this article describes the development and testing of a wearable system to measure gait cycle kinematic and kinetic parameters. The methodology for the development of the system includes the assembly of modules with commercial surface electromyography (sEMG) sensors and inertial measurement sensors, as well as the use of instrumented insoles with force-resistive sensors, and the design of the software to acquire, process, visualize, and store the data. The system design considers portability, rechargeable battery power supply, wireless communication, acquisition speed suitable for kinematic and kinetic signals, and compact size. Also, it allows simultaneous assessment of sEMG activity, hip and knee joint angles, and plantar pressure distribution, using a wireless connection via Wi-Fi and user datagram protocol for data transmission with a synchronization accuracy of 576 μs, data loss of 0.8%, and autonomy of 167 min of continuous operation, enabling uninterrupted data acquisition for gait analysis. To demonstrate its performance, the system was tested on 10 subjects without any neuromusculoskeletal pathology in indoor and outdoor environments, evaluating relevant parameters that facilitate a comprehensive analysis of gait in various contexts. The system offers a reliable, versatile, and affordable alternative for gait assessment in outdoor and indoor environments.

The manufacturing industry, notably the aeronautics sector, involves tasks presenting risks of low back pain. One of the preventive strategies could be the use of passive back exoskeletons, which have demonstrated benefits during activities involving trunk bending. This study aims to evaluate the effects of four passive back exoskeletons on trunk neuromuscular activity, kinematics, and perceived discomfort during polishing tasks simulated in a laboratory setting. Nineteen participants performed four tasks (two static bending tasks and two load-carrying tasks) without and with two soft (CORFOR and BionicBack) and two rigid (BackX and Laevo FLEX) exoskeletons. The results showed varying effects depending on the tested exoskeleton model, beyond the distinction between rigid and soft designs. Reductions in lumbar erector spinae (LES) neuromuscular activity were observed with Laevo FLEX and CORFOR during static tasks compared to the condition without exoskeleton (8-18%; p < .05). However, reductions in LES muscle activity were not significant during load carrying. Biceps femoris neuromuscular activity was significantly lower in the four tasks when using the Laevo FLEX, with reductions ranging from 8 to 17% (p < .01). The two rigid exoskeletons decreased perceived back discomfort across all tasks (p < .05). Finally, the BionicBack exoskeleton significantly altered participants' kinematics across all four tasks, reducing both trunk range of motion and average flexion (p < .05). The Laevo FLEX exoskeleton was the only one to significantly reduce both neuromuscular activity and perceived back discomfort, while causing no adverse effects, appearing advantageous when polishing in the aeronautical industry.

Wearable devices placed in or around the ear, often referred to as hearables, are gaining attention as alternative tools for pseudo-continuous health monitoring. Among their several capabilities, hearables are primarily useful for monitoring brain activity electronically via electroencephalography (EEG), enabling noninvasive, long-term recording of neural signals (e.g., from the ear canal). In addition to EEG, hearables can monitor heart rate, oxygen saturation, and temperature, all while maintaining the comfort and discretion of everyday items like earplugs or headphones. This review explores recent progress in combining multiple sensors, leveraging artificial intelligence (AI), and developing novel materials that make hearables more accurate, practical, and comfortable. On-device AI enables real-time, personalized insights that can support therapeutic interventions for neurological disorders like epilepsy. We seek further improvements in design and materials beyond this proof-of-concept, including three-dimensional printing with flexible electrodes while maintaining the unique property of monolithic circuit integration during system printing. That helps devices conform even better to the ear's anatomy for enhanced comfort and signal quality, while the rigidity of the main structure ensures a highly durable and reliable product suitable for everyday life. In particular, personalization through additive manufacturing enables custom-fitted hearables based on each user's unique ear canal features, supporting long-term wearability and reliable EEG acquisition. This review also addresses key challenges like motion artifacts and miniaturization, and current strategies to overcome them. Overall, this review highlights hearables as a key emerging technology, especially for EEG-based brain monitoring, offering a personalized, continuous, and noninvasive approach to future healthcare.

Using wearable sensors to evaluate workers' performance is challenging with existing sensor techniques. It requires detecting not only limb motions but also the onset and offset of specific actions. Commonly used inertial measurement units (IMUs) can be combined with surface electromyography (sEMG) to detect muscular activity. However, sEMG requires skin preparation and careful sensor placement, and can be affected by sweat or motion artifacts. To address these limitations, we used a wearable system combining IMUs and force-sensing resistors (FSRs), where IMUs capture joint kinematics and FSRs detect grasping actions. The system included three IMUs (on the trunk, upper arm, and forearm) and two FSR arrays (on the upper and lower arms). The system was first validated in a laboratory setting against an optical motion capture system with 10 healthy young adults performing isolated upper limb movements and mimicking lifting tasks. The results showed high agreement in joint angle estimation (coefficient of multiple correlation = 0.95 0.04), with a maximum root mean square error of 8.7 2.92°, and a mean absolute timing error for grasp detection of -0.59 seconds. To evaluate its applicability in real-world scenarios, a pilot in-field test was then conducted with two manufacturing workers (using and not using a passive shoulder exoskeleton) during a repetitive panel-packing task. The test shows highly consistent grasping detection, which allowed segmenting the task with a small variability in task duration (maximum coefficient of variation = 5.16). These findings demonstrate the feasibility of using the proposed method in industrial environments to analyze upper limb motion and grasping activity.

[This corrects the article DOI: 10.1017/wtc.2025.10022.].

Individuals with cerebral palsy (CP) experience significant impairments in lower limb mobility, which severely limit their daily activities and overall quality of life. Robotic exoskeletons have emerged as a cutting-edge solution to assist in the rehabilitation of individuals with CP by improving their motor functions. This systematic review, conducted following PRISMA guidelines, critically evaluates lower limb robotic exoskeletons specifically designed for individuals with CP, focusing on their design, rehabilitation interfaces, and clinical effectiveness. The review includes research papers published between 2010 and 2024, analyzing 30 lower limb exoskeletons reported in 57 papers. We analyze each exoskeleton, focusing on its technological features, user experience, and clinical outcomes. Notably, we identify a trend in which researchers are increasingly adapting exoskeleton functions to the specific needs of individual users, facilitating personalized rehabilitation approaches. Additionally, we highlight critical gaps in current research, such as the lack of sufficient long-term evaluations and studies assessing sustained therapeutic impacts. While ease of use remains crucial for these devices, there is a pressing need for user-friendly designs that promote prolonged engagement and adherence to therapy. This comprehensive review of existing gait rehabilitation exoskeleton technologies aimed to inform future design and application, ultimately contributing to the development of devices that better address the needs of individuals with CP and enhance their motor functions and quality of life.