Journal of Rehabilitation and Assistive Technologies Engineering最新文献

Regular use of standardized observational tools to assess nonverbal pain behaviors results in improved pain care for older adults with severe dementia. While frequent monitoring of pain behaviors in long-term care (LTC) is constrained by resource limitations, computer vision technology has the potential to mitigate these challenges. A computerized algorithm designed to assess pain behavior in older adults with and without dementia was recently developed and validated using video recordings. This study was the first live, real-time evaluation of the algorithm incorporated in an automated system with community-dwelling older adults in a laboratory. Three safely-administered thermal pain tasks were completed while the system automatically processed facial activity. Receiver Operating Characteristic curves were used to determine the sensitivity and specificity of the system in identifying facial pain expressions using gold standard manual coding. The relationship between scoring methods was analyzed and gender differences were explored. Results supported the potential viability of the system for use with older adults. System performance improved when more intense facial pain expressiveness was considered. While average pain scores remained homogenous between genders, system performance was better for women. Findings will be used to further refine the system prior to future field testing in LTC.

Background: Spinal cord injury and stroke are neurological disorders that lead to aerobic deconditioning and increased likelihood of cardiovascular disease. Sessions of at least 20 minutes of moderate-to-vigorous intensity exercise is recommended but decreased mobility limits engagement in such exercise. The aim of the study was to assess whether individuals can achieve exercise recommendations with the assistance of an end-effector robot assisted gait trainer (E-RAGT). Methods: We conducted an observational study during which participants were required to complete one exercise session on an E-RAGT. Heart rate, dyspnea, lower limb fatigue and enjoyment were monitored. Based on current guidelines, participants were considered to have reached exercise recommendations if they reached prespecified intensity thresholds for at least 20 minutes. Results: Five people with spinal cord injury and ten people with stroke agreed to participate. Every participant was able to reach moderate-to-vigorous exercise intensity and 3 out of 5 participants with spinal cord injury and 4 out of 10 participants with stroke were able to maintain this intensity for at least 20 minutes. Overall, participants rated their session as very enjoyable with a median score of 4 out of 5 on a Likert Scale (IQR 3 to 4). Discussion: Individuals with spinal cord injury or stroke who use an E-RAGT can reach moderate-to-vigorous exercise intensity, but not all are able to maintain this intensity of exercise according to disease-specific recommendations. Future research should explore the effectiveness of a training program using an E-RAGT.

The global population of older adults has increased, leading to a rising number of older adults in nursing homes without adequate care. This study proposes a smart wearable device for detecting and classifying abnormal behaviour in older adults in nursing homes. The device utilizes artificial intelligence technology to detect abnormal movements through behavioural data collection and target positioning. The intelligent recognition system and hardware sensors were tested using cloud computing and wireless sensor networks (WSNs), comparing their performance with other technologies through simulations. A triple-axis acceleration sensor collected motion behaviour data, and Zigbee enabled the wireless transfer of the sensor data. The Backpropagation (BP) neural network detected and classified abnormal behaviour based on simulated sensor data. The proposed smart wearable device offers indoor positioning, detection, and classification of abnormal behaviour. The embedded intelligent system detects routine motions like walking and abnormal behaviours such as falls. In emergencies, the system alerts healthcare workers for immediate safety measures. This study lays the groundwork for future AI-based technology implementation in nursing homes, advancing care for older adults.

Introduction: This study aimed to quantify the relationship between prosthetic users' emotional response to prosthesis aesthetics and specific product properties. Methods: Words representing prosthesis users' emotional response (Kansei) to different aesthetic designs of prostheses were identified via interviews and mood boards. A group of experts consolidated the words into thematic groups, each represented by a single, high-level 'Kansei' word. 53 lower limb prosthesis users completed a questionnaire, rating their perception of 13 aesthetic designs using the 'Kansei' words. Quantification Theory Type 1 was applied to explore the relationship between words and product properties. Sub-analyses assessed for differences based on sex, age and level of extroversion. Results: 5 high-level Kansei words were identified ('Natural', 'Technological', 'Cool', 'Unique' and 'Functional'). The Kansei word 'Natural' had a strong association with realistic looking prostheses while the words 'Technological', 'Cool' and 'Unique' were strongly associated with expressive designs which incorporate hard, colourful covers. The word 'Functional' was not a reliable predictor of product properties. No major differences were observed within sub-grouped categories. Conclusion: Kansei words identified in this study can be used to help guide clients in their aesthetic design choices and to assist designers in achieving the desired response from their products.

Public opinion may influence the adoption of technologies for older adults, yet studies on different contexts of technology for older adults is limited. In an online YouGov survey (N = 500) with text-and-image vignettes, participants gave more positive ratings of social acceptability, trust, and perceived impact on eldercare when the voice assistant ("VA" system) shown in the vignette performed a functional task (medication adherence) versus when it performed a social task (companionship). The VA received more positive sentiment comments when it appeared to use a machine learning (ML)-based dialogue system compared to when it appeared to be using a rule-based dialogue system. These results may assist designers and stakeholders select what type of voice system to develop or use with older adults.

Background: Electrical stimulation of the spinal cord may improve rewiring of the affected pathways. Immediate modulation of stimulation parameters, and its effects of it on kinematics and electromyographic variables is unclear.

Methods: This study piloted the safety and feasibility of the Reynolds Innovative Spinal Electrical Stimulation (RISES) technology with a focus on its novel closed-loop setting. This personalized, task-specific non-invasive stimulation system enables real-time stimulation parameter modulation and supports multi-data acquisition and storage. Four SCI participants underwent a clinical trial coupled with activity-based training. Primary safety outcome measures included adverse events (AEs) and skin integrity; secondary measures were vital signs, pain, and fatigue assessed at the pre, mid, and post-stimulation sessions. The trial included open-loop and closed-loop blocks of transcutaneous spinal cord stimulation (tSCS).

Results: Results showed no serious adverse events, with skin integrity unaffected. Vital signs and pain showed no significant differences across session timepoints. Fatigue levels differed significantly with post-session > mid-session > pre-session. Comparisons between open-loop and closed-loop blocks showed no significant differences in setup time, vital signs, pain, or fatigue. Average stimulation duration per task was significantly longer for open-loop (467.6 sec) than Closed-loop (410.8 sec).

Conclusions: RISES, demonstrated safety and feasibility. Further work will focus on clinical efficacy.

Introduction: Practice of ankle-foot orthoses (AFO) provision for ambulatory children with cerebral palsy is underreported and the literature is not consistent on choice of AFO-design. This study describes clinical practice of AFO provision for children with cerebral palsy and evaluates how clinical practice aligns with existing recommendations.

Methods: An online, cross-sectional survey was conducted, inviting all Norwegian orthotists working with children with cerebral palsy. Orthotic practice was investigated using a self-reported survey design.

Results: From all eligible orthotists, 54% responded, revealing that AFO provision involves patients, physicians, and physiotherapists at different stages. Patient preference directly influenced the ultimate AFO-design. Shank vertical angle was evaluated by 79%. For children with crouch gait and those with short gastrocnemius, a majority preferred a combination of rigid and articulated/flexible AFO-designs. Instrumented gait analysis was conducted by 51% at AFO delivery stage.

Conclusions: The findings show that AFO provision in Norway is collaborative, involving clinical team members and consideration of patient preferences. A discrepancy between clinical practice and existing recommendations for children with crouch gait and those with short gastrocnemius is observed.

The inability to use one's hands or arms greatly restricts the ability to perform daily activities. After a developmental or acquired injury, the intensity and frequency of rehabilitation exercises are essential. To alleviate the burden on the healthcare system, robotic systems have been developed to support clinicians' interventions. However, these systems are often bulky and expensive, limiting their use to specific clinical settings and making them impractical for home use. This paper presents the development of an affordable and easy to install 2-DOF five-bar linkage robot designed to be used at home. This work aims to reduce the cost of the robot through actuation optimization, mechanical optimization and 3D printing. The architecture and links length are chosen to optimize the robot's performance in the required workspace. Using sensor feedback, impedance control algorithms and multiple types of exercise such as virtual walls guidance are implemented. Finally, a user interface was programmed to facilitate the robot's use.