Subject-Dependent Physical Activity Recognition Model Framework with a Semi-supervised Clustering Approach

Ali, Hashim; Messina, Enza; Bisiani, Roberto

doi:10.1109/ems.2013.7

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…A different approach to address the same problem includes the use of semi-supervised techniques. In a relevant study [ 52 ], a semi-supervised clustering methodology is proposed for physical activity recognition. The approach is able to capture potential shifts in the subject’s behavior, e.g., falls, with overall accuracy, while requiring a small number of labeled data.…”

Section: Discussionmentioning

confidence: 99%

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Papagiannaki

Zacharaki

Kalouris

et al. 2019

Sensors

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The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.

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Section: Discussionmentioning

confidence: 99%

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Papagiannaki

Zacharaki

Kalouris

et al. 2019

Sensors

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“…Finally, 108 articles were deemed eligible for the in-depth manual investigation to identify and articulate research results, trends, and implications. The articles are reported in Multimedia Appendix 1 [ 27 , 37 - 142 ].…”

Section: Resultsmentioning

confidence: 99%

“…The distribution of AI classes shows that semisupervised learning models had prevailed since the 2010s, with an irregular growing trend until 2017, when they compensated for the lack of a sufficient amount of labeled data for particular inputs. Concrete examples include clustering for physical activity recognition [ 37 ], finding relevant input features for improving activity recognition [ 27 ], and detecting user-object interactions from sequences of images [ 143 ].…”

Section: Resultsmentioning

confidence: 99%

“…Semisupervised and unsupervised learning classes dominate the intelligent AAL landscape. Their prevalence is because of an increase in the variety of the health and living domain and the gradual appearance of labeled input data describing related ADL and IADL the learning aimed to support [ 37 , 38 , 143 ]. Supervised approaches have been used for classification tasks [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

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Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Jovanovic¹,

Mitrov²,

Zdravevski³

et al. 2022

J Med Internet Res

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Background Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590

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“…The accelerometer and received signal strength stream were modelled using recurrent neural networks implemented as efficient echo state networks, within the reservoir computing paradigm [ 56 ]. A novel personalized recognition model framework, for physical activities, based on a semi-supervised clustering approach to avoid fixed threshold techniques and traditional clustering methods by using a single accelerometer, has been developed by Ali et al It required a limited amount of data to compute the initial centroids for clustering of physical activities and achieved an accuracy of about 93% on average with the potential capability of recognizing subjects’ behavioural shifts, falls and exceptional events [ 57 ]. A smart shirt, embedding an inertial system ( i.e.…”

Section: Mems-based Sensor Technologies For Human Centred Applicatmentioning

confidence: 99%

MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

Ciuti

Ricotti

Menciassi

et al. 2015

Sensors

136

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Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

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Subject-Dependent Physical Activity Recognition Model Framework with a Semi-supervised Clustering Approach

Cited by 7 publications

References 21 publications

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

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