Clustering-based activity classification with a wrist-worn accelerometer using basic features

Siirtola, Pekka; Laurinen, Perttu; Haapalainen, Eija; Röning, Juha; Kinnunen, Hannu

doi:10.1109/cidm.2009.4938635

Cited by 26 publications

(22 citation statements)

References 6 publications

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“…They then showed that the correlation between inferred physical activity levels to the users' daily routine was better than when using FFT-based features. This is similar in nature to an earlier study by [39], who used an Expectation Maximisation (EM)-based clustering algorithm to generate features for their classifier which they used to recognise nine sporting activities, and reported a ≈5% improvement over a standard classification approach.…”

Section: Survey Of Feature Extraction Pipelinessupporting

confidence: 79%

A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.

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Section: Survey Of Feature Extraction Pipelinessupporting

confidence: 79%

A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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“…The type of physical activity (sitting/standing, walking, running, cycling and driving) will be tracked using a smartphone with triaxial accelerometers [48-50]. The recognition is based on different features of the acceleration signal and classification is done using knn (k nearest neighbours) and QDA (quadratic discriminant analysis) classifiers.…”

Section: Methodsmentioning

confidence: 99%

Gamified physical activation of young men – a Multidisciplinary Population-Based Randomized Controlled Trial (MOPO study)

et al. 2013

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BackgroundInactive and unhealthy lifestyles are common among adolescent men. The planned intervention examines the effectiveness of an interactive, gamified activation method, based on tailored health information, peer networks and participation, on physical activity, health and wellbeing in young men. We hypothesize that following the intervention the physical activation group will have an improved physical activity, as well as self-determined and measured health compared with the controls.Methods/designConscription-aged men (18 years) attending compulsory annual call-ups for military service in the city of Oulu in Finland (n = 1500) will be randomized to a 6-months intervention (n = 640) or a control group (n = 640) during the fall 2013. A questionnaire on health, health behaviour, diet and wellbeing is administered in the beginning and end of the intervention. In addition, anthropometric measures (height, weight and waist circumference), body composition, grip strength, heart rate variability and aerobic fitness will be measured. The activation group utilizes an online gamified activation method in combination with communal youth services, objective physical activity measurement, social networking, tailored health information and exercise programs according to baseline activity level and the readiness of changes of each individual. Daily physical activity of the participants is monitored in both the activation and control groups. The activation service rewards improvements in physical activity or reductions in sedentary behaviour. The performance and completion of the military service of the participants will also be followed.DiscussionThe study will provide new information of physical activity, health and health behaviour of young men. Furthermore, a novel model including methods for increasing physical activity among young people is developed and its effects tested through an intervention. This unique gamified service for activating young men can provide a translational model for community use. It can also be utilized as such or tailored to other selected populations or age groups.Trial registrationClinicalTrials.gov Identifier: NCT01376986

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“…On this basis, an accurate activity classification technique using a single wrist-worn accelerometer has been developed [11]. In [12] a decision tree algorithm built from clusters created beforehand has been used which assumes an off-line experimental setting and pre-fixing the structure of the classifier which also implies that any future changes or evolution of the behavior by the subjects being monitored will not be correctly classified. Therefore, this algorithm is not suitable for real-time application.…”

Section: Background and Related Workmentioning

confidence: 99%

Real-time human activity recognition from wireless sensors using evolving fuzzy systems

Andréu

Angelov

2010

International Conference on Fuzzy Systems

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A new approach to real-time knowledge extraction from streaming data generated by wearable wireless accelerometers based on self-learning evolving fuzzy rule-based classifier is proposed and evaluated in this paper. Based on experiments with real subjects we collected data from 18 different classifieds activities. After preprocessing and classifying data depending on the sequence of activities regarding time, we achieved up to 99.81% of accuracy in recognizing a sequence of activities. This technique allows re-training the system as long as the application is running on the wearable intelligent/smart sensor, getting a better classification rate throughout the time without an increase of the delay in performance.

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Clustering-based activity classification with a wrist-worn accelerometer using basic features

Cited by 26 publications

References 6 publications

A Comprehensive Study of Activity Recognition Using Accelerometers

A Comprehensive Study of Activity Recognition Using Accelerometers

Gamified physical activation of young men – a Multidisciplinary Population-Based Randomized Controlled Trial (MOPO study)

Real-time human activity recognition from wireless sensors using evolving fuzzy systems

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