Technology and Disability

BACKGROUND: The United Kingdom is challenged in terms of how it provides services for the growing number of older people and the associated rise of those living with long term health conditions into old age. Demographic and technological changes present a real opportunity for the assisted living technology industry to develop new innovations to connect and enable dispersed families to provide support to their loved ones within a consumer marketplace.

OBJECTIVE: Under the dallas i-Focus programme, the Advanced Digital Institute, with partner Coventry University, developed the Warm Neighbourhoods^® AroundMe™ service to help people live at home and enable friends and family to support them using existing connected home sensor technologies to detect usual daily routines.

METHOD: A new, consumer-focussed assistive technology service was designed through co-creation. The service was piloted over 3 months within 12 personal ``neighbourhoods'' using a Living Lab methodology.

RESULTS: Results were overwhelmingly positive. Participants easily saw the AroundMe™ service as a consumer offering. Participants found the service reassuring and unobtrusive whilst promoting independence of the main user and providing support for carers.

CONCLUSIONS: It became apparent that a unique selling point of the new service was that it was distinguishable from other existing message and emergency response type services to focus on wellbeing and ``I'm okay'' information as opposed to ``I need help''. Within a consumer market it seems that people are willing to pay for peace of mind and reassurance. Success of the pilot service was due to the application of established service design principles to make the service effective and desirable and testing within a Living Lab to develop a simple service that fits technology into the daily lives of families.

This paper presents the assistive technology used to perform activity monitoring in the USEFIL (Unobtrusive Smart Environments for Independent Living) project, particularly the wrist wearable unit. USEFIL includes a number of activity monitoring devices alongside some condition specific medical devices, a dedicated electronic health record database and communication backend. The system is designed as an assistive technology to provide long-term monitoring for older people in their own home and communicate the data that is gathered into a decision support system that can be used by the older person's carers to improve their care and allow them to remain independent in their own home. The wrist wearable device developed for the USEFIL project, the various health indicators extracted from its inbuilt sensors and how these are used to understand the health and wellbeing of the older person are discussed in this paper.

BACKGROUND: There is a lack of information surrounding assistive technology in general, but in particular, surrounding fall detectors and their use. This paper describes the results specifically pertaining to access to and provision of information regarding fall detectors which arose from a previous project exploring the use of fall detectors in the West Midlands.

OBJECTIVE: To develop a range of resources to help inform and support people at risk of falling, based upon the findings of a previous project which aimed to evaluate the use of fall detectors in the West Midlands.

METHODS: A combination of focus groups and user interviews were used to explore the successful and unsuccessful use of fall detectors across the West Midlands region in the UK. A range of resources were iteratively developed based upon the findings of these user interviews.

RESULTS: There was a lack of information for both the public and health and social care professionals with regards to the availability, advantages, disadvantages, indication and contra-indications of fall detectors, as well as lack of wider information regarding falls.

CONCLUSIONS: The project developed a range of information resources based upon the project findings, which have been so far well-received by the public and health and social care professionals. Further research must be conducted to ascertain full impact.

BACKGROUND: There is limited research exploring shoulder activity in different one arm drive wheelchairs.

OBJECTIVE: To compare EMG activity in six muscles surrounding the shoulder joint during propulsion of three different modified Action3 manual one arm drive wheelchairs. Surface EMG was measured during dynamic propulsion of each wheelchair during propulsion around an indoor obstacle course.

METHODS: Seventeen non-disabled users were randomly assigned to each wheelchair in turn. The EMG data was measured using the biometrics data link system v 7.5. Total activity levels for each muscle were calculated per user per wheelchair.

RESULTS: The NuDrive produced the highest levels of activity in triceps muscle in straight running and also in biceps and pectoralis major over mats and around corners.

The Neater produced the lowest levels of activity in biceps and pectoralis major over mats and around corners. There was no significant difference in activity in the other muscles in the different wheelchairs.

CONCLUSIONS: The results indicate that fatigue could result from propelling certain one arm drive wheelchairs. They will also help to inform clinical decision making in the prescription of wheelchairs in this group. The contribution of findings would support the need to replicate the study in a user population.

French phonemes perception in noisy conditions, in the case of a Binaural Cochlear Implant (BCI) coding, is seen in the present study. In the current work, the action of binaural noise reduction algorithms is investigated, through the use of a vocoder simulation with normal hearing listeners.

Three binaural noise reduction algorithms, used in classical hearing aids, have been considered: beamformer, Doerbecker algorithm combined with Ephraim and Malah noise estimator and Doerbecker algorithm combined with Scalart noise estimator. Then a cochlear implant (CI) coding (bins grouped into frequency bands) transformed the signal at the end of the processing chain. Also, a percentage of the input signal was ``re-injected'' (added) before CI coding.

Twenty-six normal hearing subjects participated in the experiment and they listened to sessions including 3 signal-to-noise ratios, 3 re-injection coefficients; they evaluated the coded signal (phoneme recognition). Then, a noise was added to jam the signal. The noise came from five different noise angles and the speech was issued from the front (zero deg azimuth). Altogether, experimental sessions tested 150 conditions.

Best results were obtained using the beamformer algorithm. Doerbecker with Ephraim and Malah estimator led to good results; this strategy was more efficient than the Doerbecker with Scalart estimator. Results were more sensitive to the speech processing strategy than to the noise angle. Re-injection of the input signal improved the recognition. In this BCI coding environment, noise reduction algorithms led to an improvement of 20% in phoneme recognition.