A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

Background: Speech problems are common in people living with Parkinson disease (PD), limiting communication and ultimately affecting their quality of life. Voice-assisted technology in health and care settings has shown some potential in small-scale studies to address such problems, with a retrospective analysis of user reviews reporting anecdotal communication effects and promising usability features when using this technology for people with a range of disabilities. However, there is a need for research to establish users’ perspectives on the potential contribution of voice-assisted technology for people with PD. Objective: This study aims to explore the attitudes toward the use of voice-assisted technology for people with PD. Methods: A survey was approved for dissemination by a national charity, Parkinson’s UK, to be completed on the web by people living with the condition. The survey elicited respondent demographics, PD features, voice difficulties, digital skill capability, smart technology use, voice-assisted technology ownership and use, confidentiality, and privacy concerns. Data were analyzed using descriptive statistics and summative content analysis of free-text responses. Results: Of 290 participants, 79.0% (n=229) indicated that they or others had noticed changes in their speech or voice because of the symptoms of their condition. Digital skills and awareness were reported on 11 digital skills such as the ability to find a website you have visited before. Most participants (n=209, 72.1%) reported being able to perform at least 10 of these 11 tasks. Similarly, of 70.7% (n=205) participants who owned a voice-assisted device, most of them (166/205, 80.9%) used it regularly, with 31.3% (52/166) reporting that they used the technology specifically to address the needs associated with their PD. Of these 166 users, 54.8% (n=91) sometimes, rarely, or never had to repeat themselves when using the technology. When asked about speech changes since they started using it, 25% (27/108) of participants noticed having to repeat themselves less and 14.8% (16/108) perceived their speech to be clearer. Of the 290 respondents, 90.7% (n=263) were not concerned, or only slightly concerned, about privacy and confidentiality. Conclusions: Having been added to the homes of Western society, domestic voice assist devices are now available to assist those with communication problems. People with PD reported a high digital capability, albeit those who responded to a web-based survey. Most people have embraced voice-assisted technology, find it helpful and usable, and some have found benefit to their speech. Speech and language therapists may have a virtual ally that is already in the patient’s home to support future therapy provision.

Background: Speech and language therapy involves the identification, assessment, and treatment of children and adults who have difficulties with communication, eating, drinking, and swallowing. Globally, pressing needs outstrip the availability of qualified practitioners who, of necessity, focus on individuals with advanced needs. The potential of voice-assisted technology (VAT) to assist people with speech impairments is an emerging area of research but empirical work exploring its professional adoption is limited. Objective: This study aims to explore the professional experiences of speech and language therapists (SaLTs) using VAT with their clients to identify the potential applications and barriers to VAT adoption and thereby inform future directions of research. Methods: A 23-question survey was distributed to the SaLTs from the United Kingdom using a web-based platform, eliciting both checkbox and free-text responses, to questions on perceptions and any use experiences of VAT. Data were analyzed descriptively with content analysis of free text, providing context to their specific experiences of using VAT in practice, including barriers and opportunities for future use. Results: A total of 230 UK-based professionals fully completed the survey; most were technologically competent and were aware of commercial VATs (such as Alexa and Google Assistant). However, only 49 (21.3%) SaLTs had used VAT with their clients and described 57 use cases. They reported using VAT with 10 different client groups, such as people with dysarthria and users of augmentative and alternative communication technologies. Of these, almost half (28/57, 49%) used the technology to assist their clients with day-to-day tasks, such as web browsing, setting up reminders, sending messages, and playing music. Many respondents (21/57, 37%) also reported using the technology to improve client speech, to facilitate speech practice at home, and to enhance articulation and volume. Most reported a positive impact of VAT use, stating improved independence (22/57, 39%), accessibility (6/57, 10%), and confidence (5/57, 8%). Some respondents reported increased client communication (5/57, 9%) and sociability (3/57, 5%). Reasons given for not using VAT in practice included lack of opportunity (131/181, 72.4%) and training (63/181, 34.8%). Most respondents (154/181, 85.1%) indicated that they would like to try VAT in the future, stating that it could have a positive impact on their clients’ speech, independence, and confidence. Conclusions: VAT is used by some UK-based SaLTs to enable communication tasks at home with their clients. However, its wider adoption may be limited by a lack of professional opportunity. Looking forward, additional benefits are promised, as the data show a level of engagement, empowerment, and the possibility of achieving therapeutic outcomes in communication impairment. The disparate responses suggest that this area is ripe for the development of evidence-based clinical practice, starting with a clear definition, outcome measurement, and professional standardization.

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.

This study focused on the development and usability evaluation of EnCare diagnostics (ECD) and the brain fit plan (BFP) in healthy older adults, cognitively impaired and physically impaired individuals. ECD is proposed as a novel solution to cognitive assessment based on colour selection. BFP is a novel solution to personalised cognitive stimulation. The study consisted of two trials designed to evaluate the usability of the apps. Trial 1 involved 11 healthy older adults and four older adults with physical impairments who undertook ECD and mini‐mental state examination (MMSE) once per month for 4 months with only those with physical impairments also completing the BFP daily. Trial 2 involved eight older adults diagnosed with early stage dementia who completed MMSE and ECD once per month for 6 months. In Trial 1, 10 out of 11 participants enjoyed the trial and managed the usability of the app easily. A 75% drop out was observed in response to the BFP with issues of dexterity and lack of understanding on how to use the technology being the main reasons for lack of compliance. Four out of eight participants completed Trial 2 with most of the participants having no usability issues. This usability study demonstrated that ECD is highly acceptable in both healthy older adults and those with early stage dementia when given the shorter versions to accommodate their diagnosis. The BFP was not suited to this population of participants.

Background: Continual development of the social care workforce is a key element in improving outcomes for the users of social care services. As the delivery of social care services continues to benefit from innovation in assistive technologies, it is important that the digital capabilities of the social care workforce are aligned. Policy makers have highlighted the importance of using technology to support workforce learning and development, and the need to ensure that the workforce has the necessary digital skills to fully benefit from such provisions. Objective: This study aims to identify the digital capability of the social care workforce in Northern Ireland and to explore the workforce’s appetite for and barriers to using technology for learning and development. This study is designed to answer the following research questions: (1) What is the digital capability of the social care workforce in Northern Ireland? (2) What is the workforce’s appetite to participate in digital learning and development? and (3) If there are barriers to the uptake of technology for learning and development, what are these barriers? Methods: A survey was created and distributed to the Northern Ireland social care workforce. This survey collected data on 127 metrics that described demographics, basic digital skills, technology confidence and access, factors that influence learning and development, experience with digital learning solutions, and perceived value and challenges of using technology for learning. Results: The survey was opened from December 13, 2018, to January 18, 2019. A total of 775 survey respondents completed the survey. The results indicated a workforce with a high level of self-reported basic digital skills and confidence. Face-to-face delivery of learning is still the most common method of accessing learning, which was used by 83.7% (649/775) of the respondents; however, this is closely followed by digital learning, which was used by 79.0% (612/775) of the respondents. There was a negative correlation between age and digital skills (rs=−0.262; P<.001), and a positive correlation between technology confidence and digital skills (rs=0.482; P<.001). There was also a negative correlation between age and the perceived value of technology (rs=−0.088; P=.02). The results indicated a predominantly motivated workforce in which a sizable portion is already engaged in informal digital learning. The results indicated that lower self-reported basic digital skills and confidence were associated with less interest in engaging with e-learning tools and that a portion of the workforce would benefit from additional basic digital skills training. Conclusions: These promising results provide a positive outlook for the potential of digital learning and development within the social care workforce. The findings provide clear areas of focus for the future use of technology for learning and development of the social care workforce and considerations to maximize engagement with such approaches.