20,579 research outputs found

    Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

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    In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier

    Aggregating multiple body sensors for analysis in sports

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    Real time monitoring of the wellness of sportspersons, during their sporting activity and training, is important in order to maximise performance during the sporting event itself and during training, as well as being important for the health of the sportsperson overall. We have combined a suite of common, off-the-shelf sensors with specialist body sensing technology we are developing ourselves and constructed a software system for recording, analysing and presenting sensed data gathered from a single player during a sporting activity, a football match. We gather readings for heart rate, galvanic skin response, motion, heat flux, respiration, and location (GPS) using on-body sensors, while simultaneously tracking player activity using a combination of a playercam video and pitch-wide video recording. We have aggregated all this sensed data into a single overview of player performance and activity which can be reviewed, post-event. We are currently working on integrating other non-invasive methods for real-time on-body monitoring of sweat electrolytes and pH via a textile-based sweat sampling and analysis platform. Our work is heading in two directions; firstly from post-event data aggregation to real-time monitoring, and secondly, to convert raw sensor readings into performance indicators that are meaningful to practitioners in the field

    Technology inspired design for pervasive healthcare

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    Pervasive healthcare technologies are increasingly using novel sensory devices that are able to measure phenomena that could not be measured before. To develop novel healthcare applications that use these largely untested technologies, it is important to have a design process that allows proper exploration of the capabilities of the novel technologies. We focus on the technology-inspired design process that was used in the development of a system to support posture and provide guidance by nudging people, and how this has lead us to explore pervasive healthcare applications

    Stratified Transfer Learning for Cross-domain Activity Recognition

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    In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready version

    Evaluating the impact of physical activity apps and wearables: interdisciplinary review

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    Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

    Good vibrations: Guiding body movements with vibrotactile feedback

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    We describe the ongoing development of a system to support the teaching of good posture and bowing technique to novice violin players. Using an inertial motion capture system we can track in real-time a player’s bowing action and how it deviates from a target trajectory set by their music teacher. The system provides real-time vibrotactile feedback on the correctness of the student’s posture and bowing action. We present the findings of an initial study that shows that vibrotactile feedback can guide arm movements in one and two dimension pointing tasks. The advantages of vibrotactile feedback for teaching basic bowing technique to novice violin players are that it does not place demands on the students’ visual and auditory systems which are already heavily involved in the activity of music making, and is understood with little training

    SensX: About Sensing and Assessment of Complex Human Motion

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    The great success of wearables and smartphone apps for provision of extensive physical workout instructions boosts a whole industry dealing with consumer oriented sensors and sports equipment. But with these opportunities there are also new challenges emerging. The unregulated distribution of instructions about ambitious exercises enables unexperienced users to undertake demanding workouts without professional supervision which may lead to suboptimal training success or even serious injuries. We believe, that automated supervision and realtime feedback during a workout may help to solve these issues. Therefore we introduce four fundamental steps for complex human motion assessment and present SensX, a sensor-based architecture for monitoring, recording, and analyzing complex and multi-dimensional motion chains. We provide the results of our preliminary study encompassing 8 different body weight exercises, 20 participants, and more than 9,220 recorded exercise repetitions. Furthermore, insights into SensXs classification capabilities and the impact of specific sensor configurations onto the analysis process are given.Comment: Published within the Proceedings of 14th IEEE International Conference on Networking, Sensing and Control (ICNSC), May 16th-18th, 2017, Calabria Italy 6 pages, 5 figure
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