Journal of Rehabilitation and Assistive Technologies Engineering最新文献

Introduction: Traditionally, augmentative and alternative communication (AAC) user interface development has been a time-intensive process requiring expertise in software development, often excluding people who use AAC. This paper demonstrates the involvement of an end user in the design and testing of prototype AAC user interfaces (UIs) developed using a platform called the Open Source Design and Programmer Interface (OS-DPI).

Methods: Micro-analysis of in-person conversation involving an adult with intellectual and developmental disabilities who uses AAC revealed several problems related to accessing his aided AAC device. The OS-DPI was used to co-design, develop, and test UIs aimed at addressing these observed problems.

Results: Researcher efforts to independently design and develop novel UIs that addressed the problems identified in research were ineffective. Inclusive design practices led to a shared determination of required functionality and co-design of UIs with reported improvements for access and communication.

Conclusions: This paper demonstrates the potential of the OS-DPI for the co-design and development of AAC UIs to address unique needs.

The mobility of people with severe visual impairment is limited affecting their comfort and productivity. There are about 45 million people who are blind with global financial burden and annual global cost of productivity estimated to be USD411 billion according to World Health Organization report of 2024. The contributions of the people who are visually impaired to the gross domestic product (GDP) can be enhanced deploying technology. A novel sensory substitution device that enables people who are visually impaired or blind to fairly hear objects in place of seeing objects is proposed. The wearable echolocation device was developed using three ultrasonic sensors, one placed in the right, left, and middle of the goggle with each connected to a buzzer. Audible compass is implemented in the circuitry to guide the user on the direction while walking. In this configuration, the user is guided and alerted of any obstacle within the field of coverage and so can navigate to any desired destination unaided without relying on internet. The developed prototype is easy to use and does not require complex training. The echolocation device was tested by ten enrolled people that are visually impaired or blind who validated its effectiveness and efficiency. The device is comparatively low cost, noninvasive, energy efficient and mass producible.

The need for Artificial Intelligence (AI) in gerontology education is underscored by the potential benefits it offers in addressing loneliness and supporting social connection among older adults in long-term care (LTC) homes. While the workforce in LTC is often overburdened, AI-enabled service robots present possible solutions to enhance residents' quality of life. However, the incorporation of AI and service robots in current gerontology curricula is lacking, and the views of students on this subject remain largely unexamined. This study aims to fill this gap by capturing the perspectives of university students regarding the deployment of an AI robot, Aether, in LTC environments. In this qualitative study, we conducted focus groups with 22 university students. Our thematic analysis identified three themes: (1) Aether's anticipated usage in geriatric care: companionship support and complementing the work of staff; (2) Challenges in using Aether: technical and emotional challenges; and (3) Solutions and future possibilities: expanding functionalities and integrating personalization features. Students expressed a positive outlook on the role of AI-enabled robots while also highlighting the necessity of addressing ethical and practical considerations. To effectively integrate AI technology in gerontology education, it is essential to understand and incorporate student perspectives, attitudes, and opinions.

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.