Teaching assistants' competencies and the community of inquiry at an open distance learning institution

This paper reports on qualitative research conducted in one department at an Open Distance Learning Institution. The participants were 21 e-tutors/teaching assistants (TAs) who facilitated an online compulsory module for certificate and degree seeking students in Education called “Being a Professional Teacher (BPT)”. It is a bridging course aimed at students who wish to register for a degree in education for the first time. The TAs who facilitated the BPT programme had tacit assumptions about the pedagogy (classroom based) they would use in the online environment. To help both the TAs and their trainer, the researcher conducted this initial research with a focus on the skills/competencies needed by the TAs. Through the lens of competencies, it was possible to place the study within a larger inquiry into the question about their teaching practices in an online environment. Adaptation, modification and deconstruction of teaching practices require change, which takes time and reflection. In low context communication environments, such as an e-learning environment, that change is expected to be rapid. The rapidity of change is dependent on socio-cognitive and socio-affective development of the persons who must change. The TAs did adapt quite rapidly. The three presences needed for a Community of Inquiry to be successful, namely teaching, social and cognitive presences were indicated as developing through a discussion of the competencies the TAs started with, developed during the teaching of BPT and what they still needed to acquire in the future

Toward Sensor-Based Early Diagnosis of Cognitive Impairment of Elderly Adults in Smart-Home Environments using Poisson Process Models

Emerging sensor-based assessment in combination with machine learning methodologies provide the potential to revolutionize current practices of (early) diagnosis of dementia. The goal of this research is to detect cognitive impairment in elderly adults using sensor-based measures. Longitudinal time-series data of sensor signals are analyzed with advanced computational models and supervised machine learning algorithms to identify individuals with cognitive impairment. This research further designs novel computational models using Poisson Processes that can model subtle temporal changes in sensor-based measurements, therefore have the potential to provide more reliable descriptors of cognitive impairments compared to aggregate time-series measures. Our results indicate that the proposed approach can effectively distinguish between dementia and MCI based on the sensor features yielded by the Poisson Process. Sensor-based assessment that relies on the non-homogeneous Poisson Process is further found to be effective in differentiating between adults with dementia and healthy adults, and depicts better performance compared to expert-based assessment. Findings from this research have the potential to help detect the early onset of cognitive impairment for elderly adults, and demonstrate the ability of advanced computational models and machine learning techniques to predict one’s cognitive health, thus, contributing toward advancing aging-in-place

A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study

IntroductionAssessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.MethodsA study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 – Meal, Task 2 – Beverage and Task 3 – Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.ResultsThe composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature “Activity Duration” in Task 1 – Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.DiscussionThis ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers

Longitudinal Assessment of Daily Routine Uniformity in a Smart Home Environment Using Hierarchical Clustering

The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder’s routine can potentially be of significant clinical importance. This research focuses on the assessment of changes in a person’s daily routine using longitudinal data recorded from a network of non-intrusive motion sensors in a smart home environment. We propose to identify repeating patterns in a person’s daily routine over the span of multiple days using hierarchical clustering algorithms, which allow us to disregard noisy signal patterns and various confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably quantify the degree of routinness compared to baseline approaches that compare the routines of two consecutive days or capture variations in the occurrence of recognised activities

Ambient assisted living deployment aims to empower people living with dementia (AnAbEL)

Ambient Assisted Living aims to support the wellbeing of people with special needs by offering assistive solutions. Those systems focused on dementia claim to increase the autonomy of people living with dementia by monitoring their activities. Thus, topics such as Activity Recognition related to dementia and specific solutions such as reminders and tracking users by Global Positioning System offer great advances that seek users' safety and to preserve their healthier lifestyle. However, these solutions address secondary parties by providing useful activities logs or alerts but excluding the main interested user: the person living with dementia. Although primary users are taken into consideration at some design stages by using user-centred design frameworks, final products tend not to fully address the user's needs. This paper presents an Ambient Intelligent system aimed to reduce this limitation by developing a final solution more strongly focused on enhancing a healthy lifestyle by empowering the user's autonomy. Through continued activities monitoring in real-time, the system can provide reminders to the users by coaching them to keep healthy routines. Continuous monitoring also provides a complete user's behaviour tracking and the context-awareness logic used involves the caregivers through alerts when necessary to ensure the user's safety. This article describes the process followed to develop the system aimed to cover the previous concerns and the practical feedback from health professionals over the system deployment working in a real environment

A systematic review

Purpose: Cognitive instrumental activities of daily living are particularly related to executive functions, such as scheduling appointments, monthly payments, managing the household economy, shopping or taking the bus. The aim of this systematic review was to determine the available tests for the assessment of executive functions with ecological validity to predict individuals’ functioning. Materials and methods: An electronic search was conducted in MEDLINE, Cochrane Central, PsyCInfo and IEEE Xplore until May 2019, in addition to a manual search. The PRISMA criteria and the Covidence platform were used to select articles and extract data. Results: After applying the search selection criteria, 76 studies were identified. They referred to 110 tools to assess instrumental activities of daily living. Those that have received most attention are related to menu preparation and shopping. Performance-based measures are the most widely used traditional methods. Most tests were aimed at the adult population with acquired brain damage, cognitive impairment or dementia. There was a predominance of tests based on the Multiple Errands Test paradigm. Conclusions: In recent years, it has increased the number of tools that assess the instrumental activities of daily living based on technologies such as personal or environmental sensors and serious games

Interwoven Waves:Enhancing the Scalability and Robustness of Wi-Fi Channel State Information for Human Activity Recognition

This PhD dissertation investigates the future of unobtrusive radio wave-based sensing, specifically focusing on Wi-Fi sensing in realistic healthcare scenarios. Wi-Fi sensing leverages the analysis of multi-path reflections of radio waves to monitor human activities and physiological states, providing a scalable solution without intruding on daily life.Wi-Fi-based sensing, particularly through channel state information, fits well in healthcare due to its ubiquitous presence and unobtrusiveness. As our society ages and populations grow, continuous health monitoring becomes increasingly critical. Existing solutions like wearable devices, audiovisual technologies, and expensive infrastructure modifications each have limitations, such as forgetting to wear devices, privacy invasions, and high costs. Channel state information-based sensing offers a promising alternative, enabling remote monitoring without the need for additional infrastructure changes.Nevertheless, implementing channel state information-based sensing in already congested Wi-Fi bands could present challenges in the future. Current solutions often exacerbate congestion by adding random noise, which can degrade network performance. These solutions also tend to address niche problems in idealistic settings, making it difficult to justify their use in everyday environments due to potential impacts on network latency and overall user experience.To realise the potential of Wi-Fi sensing, future solutions must integrate seamlessly with wireless communication networks, ensuring that sensing and communication processes coexist and collaborate effectively. This dissertation categorises the relationship between sensing and communication into three models: parasitic, opportunistic, and mutualistic. In the parasitic model, sensing operates independently of the wireless infrastructure, potentially adding noise and congestion. The opportunistic model leverages existing traffic flows, avoiding adverse effects on communication. The mutualistic model aims for a balance, enhancing both sensing and communication without compromising either function.The primary research objective is to enhance the robustness and scalability of channel state information-based sensing for human activity recognition, facilitating seamless integration into home environments with minimal impact on existing infrastructure. Overall, this dissertation provides an exploration of the challenges and solutions for unobtrusive Wi-Fi sensing in healthcare, paving the way for future advancements in the field