ABSTRACT
The aim of this study is to evaluate the acceptance and attitudes toward assistive technology (AT) for people with stroke from their caregivers' perspectives in Jordan. Also, this study further validates the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. This is a cross-sectional study that includes a three-sections survey: the first section contains demographic data, the second section includes the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire, and the third section consists of two optional open-ended questions. Among 123 caregivers of people with stroke over 18 years of age who participated in this study, 23% were male. The findings indicate a moderate to high acceptance of using AT for stroke survivors, confirm the validity and reliability of the UTAUT questionnaire, and clarify novel issues regarding AT based on the caregivers' perspectives.
ABSTRACT
OBJECTIVES: Universal mask wearing due to COVID-19 has introduced barriers to clear communication. In hearing impaired individuals this can impact informed surgical consent. For cochlear implant candidates, who do not rely on sign language, real-time transcription with a stenographer (CART) is the gold-standard in assistive technologies. If CART is not available, speech to text (STT) applications have been advertised as solutions, but their transcription accuracy with or without an N95 mask is not well-established. Herein, we sought to investigate the transcription accuracy of three STT solutions for iPhone and compare their performance to the CART service at our institution. METHODS: Three native English speakers and three non-native English speakers read two passages (a cochlear implant consent and the non-medical 'Rainbow passage') with and without an N95 mask. Error rates from the comparison of the transcript (from either the STT app or CART) with the original passage were calculated. RESULTS: The CART service had the lowest error rate of all testing conditions (4.79-7.14%). Ava 24/7 (15.0 ± 9.49%) and the iPhone dictation (15.6 ± 6.65%) had significantly lower average error rates than the Live Transcribe (37.7 ± 20.3%) (P < 0.0001) application. Neither the presence of an N95 nor the type of passage had a statistically significant impact on the error rate. CONCLUSION: CART should be used to augment communication with patients who are hard of hearing. If CART is not available, a STT application such as Ava 24/7 or the native iPhone dictation application may be considered, even in the context of medical terminology.
ABSTRACT
Visually impaired people require support with regular tasks including navigating, detecting obstacles, and maintaining safety, especially in both indoor and outdoor environments. As a result of the advancement of assistive technology, their lives have become substantially more convenient. Here, cutting-edge assistive devices and technologies for the visually impaired are reviewed, along with a chronology of their evolution. These methodologies are classified according to their intended applications. The taxonomy is combined with a description of the tests and experiments that can be used to examine the characteristics and assessments of assistive technology. In addition, the algorithms used in assistive devices are examined. This paper looks at solar industry innovations and promotes using renewable energy sources to create assistive devices, as well as, addresses the sudden advent of COVID-19 and the shift in the development of assistive devices. This review can serve as a stepping stone for further research on the topic.
ABSTRACT
This study proposes a new assistive device that ensures consistency and improves safety in sample collection for detecting upper respiratory diseases such as COVID-19. The COVID-19 pandemic and the various mutations of the coronavirus have increased the demand for swab-sampling–based specimen collection. Currently, the most accurate method of collecting respiratory specimens is to insert a cotton swab through the nasal passage and touch the nasopharyngeal wall. Here, the success of sampling is subjectively determined by the force applied to the swab;test results may therefore vary based on the degree of training of the medical staff, and excess force can sometimes cause pain and aftereffects in patients. Therefore, we developed a device that measures the force applied to the swab when it is inserted up to the nasopharyngeal wall and indicates this to medical staff with an audiovisual signal. In this study, we introduce the details of the sample collection devices and validate the developed device through phantom model experiments. The results of five model experiments confirm that when both visual and auditory signals were given to medical staff, more consistent swab sampling was achieved than the conventional method without signals. © ICROS 2022.
ABSTRACT
After an initial lock-down phase in 2020, the measures subsequently adopted in Italy to limit the spread of the SARS-CoV-2 virus made it impossible for people residing in nursing homes to have contacts with the outside world. Here we want to report how the DAT (Home automation, Assistive Technology and Occupational Therapy) service of Fondazione Don Carlo Gnocchi in Milan, Italy, was able to give continuity to the provision of assistive technology assessment services for older adults with disability who were not able to reach the DAT premises due to social restrictions. In the period 2020–2021, remote AT assessments were organized using a Tele-rehabilitation platform so that the physiatrist in charge of prescribing the assistive devices, supported by a therapist specialized in AT, could connect remotely from the DAT service to nursing homes in the Milan area. There, a rehabilitation professional would participate to the visit. 94 older adults with disability in 2020 and 2021 were able to take advantage of mobility assistive technology assessments provided remotely by DAT service AT professionals. The assistive devices prescribed were found to be appropriate, only a small percentage needed to be revised or were refused. High levels of satisfaction with the service provided were measured through KWAZO questionnaire among users of the service. 80% of the interviewees declared that it might be useful to keep the possibility of carrying out remote AT assessments in the future, even when the state of emergency for the COVID-19 pandemic will be finished. © 2022, The Author(s).
ABSTRACT
The proportion of elderly people in society is predicted to continue to rise in the coming decades. Mobility is a key aspect of many daily activities, but falls become an increasingly significant health risk with age. With the COVID-19 pandemic, many elderly users prefer or require assistive devices, rather than human support, in walking and carrying out daily tasks. However, prior work has shown that when using passive assistive mobility devices, fall risks can actually increase. This presents an opportunity for assistive robots to help maintain and improve the mobility of elderly users, with an additional emphasis on safety, made possible through sensing capabilities. In this paper, we present a computer vision system that detects the eye blink and face angle patterns for exhibiting signs of tiredness. In addition to the frame-based detection, we also introduce a time-window collation with a machine learning classifier. The system proposed here is critical in monitoring the user, performing real-time detection, and recommending they take a break if tiredness is detected. The overall system architecture and algorithmic details are presented, then a series of experiments are conducted to validate the performance of the approach. © 2022 IEEE.
ABSTRACT
Whenever the patient is admitted to a hospital, the doctors and nurses have to keep a regular check on each of them and also maintain a record of all the vital health parameters of patient. In a situation of pandemic like the one due to COVID-19 a huge number of patients are admitted at once which becomes very difficult for hospital staff to monitor each of them continuously. So, the model proposed here is a IoT based wearable system which is an assistive device for patient monitoring which will help the doctors, nurses and patient's relatives to keep a track of some vital health parameters like heartbeat, temperature and blood oxygen levels. All of these sensor data will be stored into the cloud platform "Thingspeak"and also to the mobile app "Blynk". And this system is made automatic so it will keep on monitoring and storing the sensor data at regular intervals which can be accessed from anywhere via the internet and even the alert system will notify about the emergency. Another part of the model which is separate from the wearable system is to monitor and store volume of drip or IV fluids is given to the patient. © 2022 Copyright for this paper by its authors.
ABSTRACT
As the percentage of the older adults population increases worldwide, both the risk of social isolation of the older adults and the shortage of nursing care personnel have become major issues. To address these issues, we have developed a recreational program, the scenario-type robot-assisted recreation, to promote communication among the older adults in aged care facilities. While the burden of the facility staff increases due to the COVID-19 pandemic, we improved the robot operation interface, developed a system to support the operation of this activity by the facility staff from a remote location, and developed a system to realize communication with remote family members, in order to smoothly operate this recreational activity by the facility staff by themselves. © 2022 IEEE.