Codevelopment and Deployment of a System for the Telemonitoring of Activities of Daily Living Among Older Adults Receiving Home Care Services: Protocol for an Action Design Research Study.

BACKGROUND: Telemonitoring of activities of daily living (ADLs) offers significant potential for gaining a deeper insight into the home care needs of older adults experiencing cognitive decline, particularly those living alone. In 2016, our team and a health care institution in Montreal, Quebec, Canada, sought to test this technology to enhance the support provided by home care clinical teams for older adults residing alone and facing cognitive deficits. The Support for Seniors' Autonomy program (SAPA [Soutien à l'autonomie des personnes âgées]) project was initiated within this context, embracing an innovative research approach that combines action research and design science. OBJECTIVE: This paper presents the research protocol for the SAPA project, with the aim of facilitating the replication of similar initiatives in the future. The primary objectives of the SAPA project were to (1) codevelop an ADL telemonitoring system aligned with the requirements of key stakeholders, (2) deploy the system in a real clinical environment to identify specific use cases, and (3) identify factors conducive to its sustained use in a real-world setting. Given the context of the SAPA project, the adoption of an action design research (ADR) approach was deemed crucial. ADR is a framework for crafting practical solutions to intricate problems encountered in a specific organizational context. METHODS: This project consisted of 2 cycles of development (alpha and beta) that involved cyclical repetitions of stages 2 and 3 to develop a telemonitoring system for ADLs. Stakeholders, such as health care managers, clinicians, older adults, and their families, were included in each codevelopment cycle. Qualitative and quantitative data were collected throughout this project. RESULTS: The first iterative cycle, the alpha cycle, took place from early 2016 to mid 2018. The first prototype of an ADL telemonitoring system was deployed in the homes of 4 individuals receiving home care services through a public health institution. The prototype was used to collect data about care recipients' ADL routines. Clinicians used the data to support their home care intervention plan, and the results are presented here. The prototype was successfully deployed and perceived as useful, although obstacles were encountered. Similarly, a second codevelopment cycle (beta cycle) took place in 3 public health institutions from late 2018 to late 2022. The telemonitoring system was installed in 31 care recipients' homes, and detailed results will be presented in future papers. CONCLUSIONS: To our knowledge, this is the first reported ADR project in ADL telemonitoring research that includes 2 iterative cycles of codevelopment and deployment embedded in the real-world clinical settings of a public health system. We discuss the artifacts, generalization of learning, and dissemination generated by this protocol in the hope of providing a concrete and replicable example of research partnerships in the field of digital health in cognitive aging. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/52284.

Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention.

Deep learning models have gained prominence in human activity recognition using ambient sensors, particularly for telemonitoring older adults' daily activities in real-world scenarios. However, collecting large volumes of annotated sensor data presents a formidable challenge, given the time-consuming and costly nature of traditional manual annotation methods, especially for extensive projects. In response to this challenge, we propose a novel AttCLHAR model rooted in the self-supervised learning framework SimCLR and augmented with a self-attention mechanism. This model is designed for human activity recognition utilizing ambient sensor data, tailored explicitly for scenarios with limited or no annotations. AttCLHAR encompasses unsupervised pre-training and fine-tuning phases, sharing a common encoder module with two convolutional layers and a long short-term memory (LSTM) layer. The output is further connected to a self-attention layer, allowing the model to selectively focus on different input sequence segments. The incorporation of sharpness-aware minimization (SAM) aims to enhance model generalization by penalizing loss sharpness. The pre-training phase focuses on learning representative features from abundant unlabeled data, capturing both spatial and temporal dependencies in the sensor data. It facilitates the extraction of informative features for subsequent fine-tuning tasks. We extensively evaluated the AttCLHAR model using three CASAS smart home datasets (Aruba-1, Aruba-2, and Milan). We compared its performance against the SimCLR framework, SimCLR with SAM, and SimCLR with the self-attention layer. The experimental results demonstrate the superior performance of our approach, especially in semi-supervised and transfer learning scenarios. It outperforms existing models, marking a significant advancement in using self-supervised learning to extract valuable insights from unlabeled ambient sensor data in real-world environments.

Predicting levels of prolonged grief disorder symptoms during the COVID-19 pandemic: An integrated approach of classical data exploration, predictive machine learning, and explainable AI.

BACKGROUND: Prior studies on Prolonged Grief Disorder (PGD) primarily employed classical approaches to link bereaved individuals' characteristics with PGD symptom levels. This study utilized machine learning to identify key factors influencing PGD symptoms during the COVID-19 pandemic. METHODS: We analyzed data from 479 participants through an online survey, employing classical data exploration, predictive machine learning, and SHapley Additive exPlanations (SHAP) to determine key factors influencing PGD symptoms measured with the Traumatic Grief Inventory - Self Report (TGI-SR) from 19 variables, comparing five predictive models. RESULTS: The classical approach identified eight variables associated with a possible PGD (TGI-SR score ≥ 59): unexpected causes of death, living alone, seeking professional support, taking anxiety and/or depression medications, using more grief services (telephone or online supports) and more confrontation-oriented coping strategies, and higher levels of depression and anxiety. Using machine learning techniques, the CatBoost algorithm provided the best predictive model of the TGI-SR score (r2 = 0.6479). The three variables influencing the most the level of PGD symptoms were anxiety, and levels of avoidance and confrontation coping strategies used. CONCLUSIONS: This pioneering approach within the field of grief research enabled us to leverage the extensive dataset collected during the pandemic, facilitating a deeper comprehension of the predominant factors influencing the grieving process for individuals who experienced loss during this period. LIMITATIONS: This study acknowledges self-selection bias, limited sample diversity, and suggests further research is needed to fully understand the predictors of PGD symptoms.

LIPSHOK: LIARA Portable Smart Home Kit.

Several smart home architecture implementations have been proposed in the last decade. These architectures are mostly deployed in laboratories or inside real habitations built for research purposes to enable the use of ambient intelligence using a wide variety of sensors, actuators and machine learning algorithms. However, the major issues for most related smart home architectures are their price, proprietary hardware requirements and the need for highly specialized personnel to deploy such systems. To tackle these challenges, lighter forms of smart home architectures known as smart homes in a box (SHiB) have been proposed. While SHiB remain an encouraging first step towards lightweight yet affordable solutions, they still suffer from few drawbacks. Indeed, some of these kits lack hardware support for some technologies, and others do not include enough sensors and actuators to cover most smart homes' requirements. Thus, this paper introduces the LIARA Portable Smart Home Kit (LIPSHOK). It has been designed to provide an affordable SHiB solution that anyone is able to install in an existing home. Moreover, LIPSHOK is a generic kit that includes a total of four specialized sensor modules that were introduced independently, as our laboratory has been working on their development over the last few years. This paper first provides a summary of each of these modules and their respective benefits within a smart home context. Then, it mainly focus on the introduction of the LIPSHOK architecture that provides a framework to unify the use of the proposed sensors thanks to a common modular infrastructure capable of managing heterogeneous technologies. Finally, we compare our work to the existing SHiB kit solutions and outline that it offers a more affordable, extensible and scalable solution whose resources are distributed under an open-source license.

Vibration of the Whole Foot Soles Surface Using an Inexpensive Portable Device to Investigate Age-Related Alterations of Postural Control.

Background: Standing on a foam surface is used to investigate how aging affect the ability to keep balance when somatosensory inputs from feet soles become unreliable. However, since standing on foam also affects the efficacy of postural adjustments, the respective contributions of sensory and motor components are impossible to separate. This study tested the hypothesis that these components can be untangled by comparing changes of center of pressure (CoP) parameters induced by standing on a foam pad vs. a novel vibration (VIB) platform developed by our team and targeting feet soles' mechanoreceptors. Methods: Bipedal postural control of young (n = 20) and healthy elders (n = 20) was assessed while standing barefoot on a force platform through 3 randomized conditions: (1) Baseline (BL); (2) VIB; and (3) Foam. CoP Amplitude and Velocity in the antero-posterior/medio-lateral (AP/ML) directions and COP Surface were compared between conditions and groups. Findings: Both VIB and Foam increased CoP parameters compared to BL, but Foam had a significantly greater impact than VIB for both groups. Young and Old participants significantly differed for all three Conditions. However, when correcting for BL levels of postural performance, VIB-related increase of COP parameters was no longer different between groups, conversely to Foam. Interpretation: Although both VIB and Foam highlighted age-related differences of postural control, their combined use revealed that "motor" and "sensory" components are differently affected by aging, the latter being relatively unaltered, at least in healthy/active elders. The combined used of these methods could provide relevant knowledge to better understand and manage postural impairments in the aging population.

Effects and Acceptability of an Individualized Home-Based 10-Week Training Program in Adults with Myotonic Dystrophy Type 1.

BACKGROUND: Muscle weakness is a cardinal sign of myotonic dystrophy type 1, causing important functional mobility limitations and increasing the risk of falling. As a non-pharmacological, accessible and safe treatment for this population, strength training is an intervention of choice. OBJECTIVE: To document the effects and acceptability of an individualized semi-supervised home-based exercise program on functional mobility, balance and lower limb strength, and to determine if an assistive training device has a significant impact on outcomes. METHODS: This study used a pre-post test design and men with the adult form of DM1 were randomly assigned to the control or device group. The training program was performed three times a week for 10 weeks and included three exercises (sit-to-stand, squat, and alternated lunges). Outcome measures included maximal isometric muscle strength, 10-Meter Walk Test, Mini-BESTest, 30-Second Chair Stand Test and 6-minute walk test. RESULTS: No outcome measures showed a significant difference, except for the strength of the knee flexors muscle group between the two assessments. All participants improved beyond the standard error of measurement in at least two outcome measures. The program and the device were well accepted and all participants reported many perceived improvements at the end of the program. CONCLUSIONS: Our results provide encouraging data on the effects and acceptability of a home-based training program for men with the adult form of DM1. These programs would reduce the financial burden on the health system while improving the clinical services offered to this population.

Fall Detection With UWB Radars and CNN-LSTM Architecture.

Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get worse with the time and conducts to painful end of life or even death. One solution is to detect falls efficiently to alert somebody (e.g., nurses) as quickly as possible. To respond to this need, we propose to detect falls in a real apartment of 40 square meters by exploiting three ultra-wideband radars and a deep neural network model. The deep neural network is composed of a convolutional neural network stacked with a long-short term memory network and a fully connected neural network to identify falls. In other words, the problem addressed in this paper is a binary classification attempting to differentiate fall and non-fall events. As it can be noticed in real cases, the falls can have different forms. Hence, the data to train and test the classification model have been generated with falls (four types) simulated by 10 participants in three locations in the apartment. Finally, the train and test stages have been achieved according to three strategies, including the leave-one-subject-out method. This latter method allows for obtaining the performances of the proposed system in a generalization context. The results are very promising since we reach almost 90% of accuracy.

Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report.

BACKGROUND: Many older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level. OBJECTIVE: The aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation. METHODS: This longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity. RESULTS: Over time, the monitoring reports showed evolving trends in the care recipient's daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation. CONCLUSIONS: Overall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse's questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making.

Highly Accurate Bathroom Activity Recognition Using Infrared Proximity Sensors.

Among elderly populations over the world, a high percentage of individuals are affected by physical or mental diseases, greatly influencing their quality of life. As it is a known fact that they wish to remain in their own home for as long as possible, solutions must be designed to detect these diseases automatically, limiting the reliance on human resources. To this end, our team developed a sensors platform based on infrared proximity sensors to accurately recognize basic bathroom activities such as going to the toilet and showering. This article is based on the body of scientific literature which establish evidences that activities relative to corporal hygiene are strongly correlated to health status and can be important signs of the development of eventual disorders. The system is built to be simple, affordable and highly reliable. Our experiments have shown that it can yield an F-Score of 96.94%. Also, the durations collected by our kit are approximately 6 seconds apart from the real ones; those results confirm the reliability of our kit.

A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition.

Many people in the world are affected by muscle wasting, especially the population hits by myotonic dystrophy type 1 (DM1). Those people are usually given a program of multiple physical exercises to do. While DM1 and many other people have difficulties attending commercial centers to realize their program, a solution is to develop such a program completable at home. To this end, we developed a portable system that patients could bring home. This prototype is an improved version of the previous one using Wi-Fi, as this new prototype runs on BLE technology. This new prototype conceptualized induces great results.

The Virtual Environment for Rapid Prototyping of the Intelligent Environment.

Advances in domains such as sensor networks and electronic and ambient intelligence have allowed us to create intelligent environments (IEs). However, research in IE is being held back by the fact that researchers face major difficulties, such as a lack of resources for their experiments. Indeed, they cannot easily build IEs to evaluate their approaches. This is mainly because of economic and logistical issues. In this paper, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs because they are inexpensive, and experiments can be conducted easily. Our simulator is open source and it provides users with a set of virtual sensors that simulates the behavior of real sensors. This simulator gives the user the capacity to build their own environment, providing a model to edit inhabitants' behavior and an interactive mode. In this mode, the user can directly act upon IE objects. This simulator gathers data generated by the interactions in order to produce datasets. These datasets can be used by scientists to evaluate several approaches in IEs.

The Light Node Communication Framework: A New Way to Communicate Inside Smart Homes.

The Internet of things has profoundly changed the way we imagine information science and architecture, and smart homes are an important part of this domain. Created a decade ago, the few existing prototypes use technologies of the day, forcing designers to create centralized and costly architectures that raise some issues concerning reliability, scalability, and ease of access which cannot be tolerated in the context of assistance. In this paper, we briefly introduce a new kind of architecture where the focus is placed on distribution. More specifically, we respond to the first issue we encountered by proposing a lightweight and portable messaging protocol. After running several tests, we observed a maximized bandwidth, whereby no packets were lost and good encryption was obtained. These results tend to prove that our innovation may be employed in a real context of distribution with small entities.