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Assistive robots have the potential to support independence, enhance safety, and lower healthcare costs for older adults, as well as alleviate the demands of their care partners. However, ensuring that these robots will effectively and reliably address end-user needs in the long term requires user-specific design factors to be considered during the robot development process. To identify these design factors, we embedded Stretch, a mobile manipulator created by Hello Robot Inc., in the home of an older adult with motor impairments and his care partner for four weeks to support them with everyday activities. An occupational therapist and a robotics engineer lived with them during this period, employing an immersive participatory design approach to co-design and customise the robot with them. We highlight the benefits of this immersive participatory design experience and provide insights into robot design that can be applied broadly to other assistive technologies. [ABSTRACT FROM AUTHOR]

Automated fall detection devices for individuals who use wheelchairs to minimize the consequences of falls are lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambulatory, young adults simulated falls from a wheelchair and performed other wheelchair-related mobility activities in a laboratory. Neural Network classifiers were used to train the algorithm developed based on data retrieved from accelerometers mounted at the participant's wrist, chest, and head. Results indicate excellent accuracy to differentiate between falls and wheelchair mobility activities. The sensors mounted at the wrist, chest, and head presented with an accuracy of 100%, 96.9%, and 94.8%, respectively, using data from 258 falls and 220 wheelchair mobility activities. This pilot study indicates that a fall detection algorithm developed in a laboratory setting based on fall accelerometer patterns can accurately differentiate wheelchair-related falls and wheelchair mobility activities. This algorithm should be integrated into a wrist-worn devices and tested among individuals who use a wheelchair in the community. [ABSTRACT FROM AUTHOR]

A reliable fall detection device is crucial to minimize long-term consequences of falls among wheelchair users. This study examines the sensitivity of Apple Watch to detect intentional falls from a wheelchair. Twenty-five able bodied (age: 21.9 ± 2.5 years) participated in a protocol in which they intentionally fell out of a wheelchair in a laboratory setting. Each participant wore an Apple Watch Series 5 and performed 3 falls in the forward, right and left sideways, and backward directions onto a crash pad totaling 12 falls each. The Apple Watch was manually checked after each fall to determine if the device registered a fall. From 300 fall trials captured, the Apple Watch detected 14 falls showing a sensitivity of 4.7%, a false negative rate of 95.3%. Logistic regression showed that participant's height, impact force, lower limb functioning, and fall direction are parameters that may influence the ability of the Apple Watch to detect falls from a wheelchair. The Apple Watch fall detection feature presented with a very poor sensitivity to detect intentional falls from a wheelchair among able bodied young adults. Due to the high incidence and consequences of falls, a reliable fall detection device specific for wheelchair users is warranted. [ABSTRACT FROM AUTHOR]

Metallic nanoparticles have been reported to have wide spread applications in the field of agricultural with a potential to enhance the activity natural substances to replace hazardous chemical fungicides and pesticides. Higher size to volume ratio, greater surface area, optimum efficacy, and immense precision are some of the benefits of employing nanotechnology in agricultural formulations. In this study, copper nano-biocomposites have been successfully synthesized using chitosan, a naturally occurring polymer and an organically available ascorbic acid. We report the appearance of the characteristic brick-brown color in aqueous solution that indicates the formation of copper nanoparticles. These particles were subjected to various physico-chemical characterizations using UV-Vis spectrophotometer, DLS, Zeta Potential, TEM, TGA, and FTIR to understand the morphology, chemical, and physical profiling. It was inferred that ascorbic acid acted as reducing agent while chitosan as capping agent to stabilize the colloidal copper nanoparticles of 20–120 nm size. In aqueous solution, the particles were observed to be monodispersed with minimum aggregation. The synthesized nanoparticles showed significant antifungal and antibacterial activity against common notorious agricultural plant pathogens, viz., Fusarium sp., Aspergillus sp., Alternaria alternata, Pythium sp., and Bacillus cereus, assayed using poisoned food method. The antimicrobial activity was showcased at a minimum concentration of 0.1–0.7% for different organisms. Furthermore, filed trial studies were performed on ginger (Zingiber officinale) plantations for a period of 8 weeks which substantiated with our lab results. These studies validated our claim of antifungal activity and simultaneously furnished us with toxicity results. The use of eco-friendly, nontoxic, biodegradable, and environmentally safe raw materials and the resultant nanoparticle solution ensure that the finished product does not induce any threat to the environment or human and shows maximum efficacy at minimum concentration. [ABSTRACT FROM AUTHOR]

Purpose: Individuals with traumatic brain injury (TBI) often experience difficulties in performing kitchen-related sequencing tasks due to cognitive deficits. The primary aim of this study is to examine the feasibility of a context-aware automatic prompting system in assisting individuals with TBI in multi-step cooking tasks. Method: Sixteen individuals with TBI participated in the study. A randomized cross-over design was used to compare the automatic prompting method with a conventional user-controlled method through a tablet device. Participant performance under each prompting method was assessed using the Performance Assessment of Self-Care Skills in terms of independence, safety, and adequacy. Subjective workload and qualitative feedback were also collected. Results: The automatic method, when compared with the user-controlled method, significantly decreased the amount of external assistance required by participants, received higher ratings in user perceived ease-of-use, and was considered less stressful for participants. However, the user-controlled method showed strengths in offering participants more flexibility in terms of controlling on the timing of prompts. Conclusions: The results provided insight into the potential benefits and user perceptions of a context-aware prompting system. The information could contribute to the future development of advanced prompting technology for people with cognitive impairments in completing sequential tasks. For people with traumatic brain injury, the context-aware prompting method showed advantages in improving user performance, receiving better ratings on ease-of-use, and decreasing stress levels, compared to the user-controlled prompting method in completing multi-step cooking tasks. Future prompting systems for people with cognitive impairments may allow users to control the pace of prompting and use sensing information as back-up assistance in critical situations. In this way, the system may help users monitor their actions and offer confirmations, especially at steps with safety concerns, thus enhancing the sense of security and reducing the stress from self-monitoring. [ABSTRACT FROM AUTHOR]