694 research outputs found
A systematic review of gerontechnologies to support aging in place among community-dwelling older adults and their family caregivers
ObjectivePaucity of information concerning the efficacy of gerontechnologies to support aging in place among community-dwelling older adults prevents potential users, healthcare professionals, and policymakers from making informed decisions on their use. The goal of this study was to identify gerontechnologies tested for home support in dyads of community-dwelling older adults with unimpaired cognition and their family caregivers, including their benefits and challenges. We also provide the level of evidence of the studies and recommendations to address the specific challenges preventing their use, dissemination, and implementation.MethodsWe conducted a systematic review of the literature published between 2016 and 2021 on gerontechnologies tested for home support in dyads. Two independent reviewers screened the abstracts according to the inclusion/exclusion criteria. A third reviewer resolved eligibility discrepancies. Data extraction was conducted by two independent reviewers.ResultsOf 1,441 articles screened, only 13 studies met the inclusion criteria with studies of moderate quality. Mostly, these gerontechnologies were used to monitor the older adult or the environment, to increase communication with family caregivers, to assist in daily living activities, and to provide health information. Benefits included facilitating communication, increasing safety, and reducing stress. Common challenges included difficulties using the technologies, technical problems, privacy issues, increased stress and dissatisfaction, and a mismatch between values and needs.ConclusionOnly a few gerontechnologies have proven efficacy in supporting community-dwelling older adults and their family caregivers. The inclusion of values and preferences, co-creation with end users, designing easy-to-use technologies, and assuring training are strongly recommended to increase acceptability and dissemination.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=310803, identifier CRD42022310803
Active and assisted living ecosystem for the elderly
A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors
Autonomous Radar-based Gait Monitoring System
Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in people’s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4].
A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinician’s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera.
This thesis presents the design and testing of a radar-based contactless system that can monitor people’s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individual’s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individual’s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong “clutter” impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6].
To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations.
The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple people’s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC).
The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices
Age estimation algorithm based on deep learning and its application in fall detection
With the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimation of the human body, a complete priority detection of the key population, and a phased single aggregation backbone network VoVNetv4 was proposed for feature extraction. At the same time, the regional single aggregation module ROSA module was constructed to encapsulate the feature module regionally. The adaptive stage module was used for feature smoothing. Consistent predictions for each task were made using the CORAL framework as a classifier and tasks were divided in binary. At the same time, a gradient two-node fall detection framework combined with age estimation was designed. The detection was divided into a primary node and a secondary node. In the first-level node, the age estimation algorithm based on VoVNetv4 was used to classify the population of different age groups. A face tracking algorithm was constructed by combining the key point matrices of humans, and the body processed by OpenPose with the central coordinates of the human face. In the secondary node, human age gradient information was used to detect human falls based on the AT-MLP model. The experimental results show that compared with Resnet-34, the MAE value of the proposed method decreased by 0.41. Compared with curriculum learning and the CORAL-CNN method, MAE value decreased by 0.17 relative to the RMSE value. Compared with other methods, the method in this paper was significantly lower, with a biggest drop of 0.51
EMERGING APPLICATIONS IN THE MEASUREMENT OF BODY COMPOSITION AND THEIR RELATIONSHIPS TO DISEASE RISK
Ph.D
Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions
The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field
Moving usable security research out of the lab: evaluating the use of VR studies for real-world authentication research
Empirical evaluations of real-world research artefacts that derive results from observations and experiments are a core aspect of usable security research. Expert interviews as part of this thesis revealed that the costs associated with developing and maintaining physical research artefacts often amplify human-centred usability and security research challenges. On top of that, ethical and legal barriers often make usability and security research in the field infeasible. Researchers have begun simulating real-life conditions in the lab to contribute to ecological validity. However, studies of this type are still restricted to what can be replicated in physical laboratory settings. Furthermore, historically, user study subjects were mainly recruited from local areas only when evaluating hardware prototypes. The human-centred research communities have recognised and partially addressed these challenges using online studies such as surveys that allow for the recruitment of large and diverse samples as well as learning about user behaviour. However, human-centred security research involving hardware prototypes is often concerned with human factors and their impact on the prototypes’ usability and security, which cannot be studied using traditional online surveys.
To work towards addressing the current challenges and facilitating research in this space, this thesis explores if – and how – virtual reality (VR) studies can be used for real-world usability and security research. It first validates the feasibility and then demonstrates the use of VR studies for human-centred usability and security research through six empirical studies, including remote and lab VR studies as well as video prototypes as part of online surveys.
It was found that VR-based usability and security evaluations of authentication prototypes, where users provide touch, mid-air, and eye-gaze input, greatly match the findings from the original real-world evaluations. This thesis further investigated the effectiveness of VR studies by exploring three core topics in the authentication domain: First, the challenges around in-the-wild shoulder surfing studies were addressed. Two novel VR shoulder surfing methods were implemented to contribute towards realistic shoulder surfing research and explore the use of VR studies for security evaluations. This was found to allow researchers to provide a bridge over the methodological gap between lab and field studies. Second, the ethical and legal barriers when conducting in situ usability research on authentication systems were addressed. It was found that VR studies can represent plausible authentication environments and that a prototype’s in situ usability evaluation results deviate from traditional lab evaluations. Finally, this thesis contributes a novel evaluation method to remotely study interactive VR replicas of real-world prototypes, allowing researchers to move experiments that involve hardware prototypes out of physical laboratories and potentially increase a sample’s diversity and size.
The thesis concludes by discussing the implications of using VR studies for prototype usability and security evaluations. It lays the foundation for establishing VR studies as a powerful, well-evaluated research method and unfolds its methodological advantages and disadvantages
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Review of substitutive assistive tools and technologies for people with visual impairments: recent advancements and prospects
YesThe development of many tools and technologies for people with visual impairment has become a major priority in the
field of assistive technology research. However, many of these technology advancements have limitations in terms of the
human aspects of the user experience (e.g., usability, learnability, and time to user adaptation) as well as difficulties in
translating research prototypes into production. Also, there was no clear distinction between the assistive aids of adults
and children, as well as between “partial impairment” and “total blindness”. As a result of these limitations, the produced
aids have not gained much popularity and the intended users are still hesitant to utilise them. This paper presents a comprehensive review of substitutive interventions that aid in adapting to vision loss, centred on laboratory research studies
to assess user-system interaction and system validation. Depending on the primary cueing feedback signal offered to the
user, these technology aids are categorized as visual, haptics, or auditory-based aids. The context of use, cueing feedback
signals, and participation of visually impaired people in the evaluation are all considered while discussing these aids.
Based on the findings, a set of recommendations is suggested to assist the scientific community in addressing persisting
challenges and restrictions faced by both the totally blind and partially sighted people
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