Intelligent services for assisting independent living of elderly people at home

Vergados, Dimitrios D.; Alevizos, Alevizos; Mariolis, Anargiros; Caragiozidis, Michael

doi:10.1145/1389586.1389677

Cited by 32 publications

(15 citation statements)

References 2 publications

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“…Furthermore, by 2020 there will be 50 billion internet-connected devices, which will surpass the projected world population of 7.6 billion; thus equating to 6.58 connected devices per person [14]. Additionally, advances in the areas of wireless communication, home automation and medical monitoring systems are also revolutionising the healthcare industry so that healthcare can be transferred from the hospital into the home [15].…”

Section: Introductionmentioning

confidence: 99%

Detecting physical activity within lifelogs towards preventing obesity and aiding ambient assisted living

2017

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A B S T R A C TObesity is a global health issue that affects 2.1 billion people worldwide and has an economic impact of approximately $2 trillion. It is a disease that can make the aging process worse by impairing physical function, which can lead to people becoming more frail and immobile. Nevertheless, it is envisioned that technology can be used to aid in motivating behavioural changes to combat this preventable condition. The ubiquitous presence of wearable and mobile devices has enabled a continual stream of quantifiable data (e.g. physiological signals) to be collected about ourselves. This data can then be used to monitor physical activity to aid in self-reflection and motivation to alter behaviour. However, such information is susceptible to noise interference, which makes processing and extracting knowledge from such data challenging. This paper posits our approach that collects and processes physiological data that has been collected from tri-axial accelerometers and a heart-rate monitor, to detect physical activity. Furthermore, an end-user use case application has also been proposed that integrates these findings into a smartwatch visualization. This provides a method of visualising the results to the user so that they are able to gain an overview of their activity. The goal of the paper has been to evaluate the performance of supervised machine learning in distinguishing physical activity. This has been achieved by (i) focusing on wearable sensors to collect data and using our methodology to process this raw lifelogging data so that features can be extracted/selected. (ii) Undertaking an evaluation between ten supervised learning classifiers to determine their accuracy in detecting human activity. To demonstrate the effectiveness of our method, this evaluation has been performed across a baseline method and two other methods. (iii) Undertaking an evaluation of the processing time of the approach and the smartwatch battery and network cost analysis between transferring data from the smartwatch to the phone. The results of the classifier evaluations indicate that our approach shows an improvement on existing studies, with accuracies of up to 99% and sensitivities of 100%.

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Section: Introductionmentioning

confidence: 99%

Detecting physical activity within lifelogs towards preventing obesity and aiding ambient assisted living

2017

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“…Today, more than three quarters of the elderly population are suffering from chronic diseases, independent of the economic, social, and cultural background [1]. Such diseases can be possibly avoided or decreased if people often undergo a medical examination and find the early symptoms of these diseases [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of Gait Anomaly due to Lesion Using Full-Body Gait Motions

Higashiguchi

Shimoyama

Ukita

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a method for evaluating a physical gait motion based on a 3D human skeleton measured by a depth sensor. While similar methods measure and evaluate the motion of only a part of interest (e.g., knee), the proposed method comprehensively evaluates the motion of the full body. The gait motions with a variety of physical disabilities due to lesioned body parts are recorded and modeled in advance for gait anomaly detection. This detection is achieved by finding lesioned parts a set of pose features extracted from gait sequences. In experiments, the proposed features extracted from the full body allowed us to identify where a subject was injured with 83.1% accuracy by using the model optimized for the individual. The superiority of the full-body features was validated in in contrast to local features extracted from only a body part of interest (77.1% by lower-body features and 65% by upper-body features). Furthermore, the effectiveness of the proposed full-body features was also validated with single universal model used for all subjects; 55.2%, 44.7%, and 35.5% by the full-body, lower-body, and upper-body features, respectively.

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“…Within the Western world, older persons typically suffer from at least one chronic condition (Vergados, Alevizos, Mariolis, & Caragiozidis, 2008). Coupled with chronic medical conditions, older persons typically display a number of more general characteristics that come with the ageing process.…”

Section: Introductionmentioning

confidence: 99%

Experiences in the design, iterative development and evaluation of a technology-enabled garment for active ageing walkers

Burns

2015

Textile-Led Design for the Active Ageing Population

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Intelligent services for assisting independent living of elderly people at home

Cited by 32 publications

References 2 publications

Detecting physical activity within lifelogs towards preventing obesity and aiding ambient assisted living

Detecting physical activity within lifelogs towards preventing obesity and aiding ambient assisted living

Classification of Gait Anomaly due to Lesion Using Full-Body Gait Motions

Experiences in the design, iterative development and evaluation of a technology-enabled garment for active ageing walkers

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