@article{
 title = {Detecting physical activity within lifelogs towards preventing obesity and aiding ambient assisted living},
 type = {article},
 keywords = {activity,activity-recognition,lifelogging,obesity,physical,signal-processing},
 publisher = {Elsevier},
 id = {4b599c6f-8b8a-3db9-bc1a-8e17a5fe94fb},
 created = {2018-07-12T21:31:55.118Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:31:55.118Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {DobbinsDetecting},
 source_type = {article},
 private_publication = {false},
 abstract = {Obesity 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%.},
 bibtype = {article},
 author = {Dobbins, C and Rawassizadeh, R and Momeni, E},
 journal = {Neurocomputing}
}

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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. 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