A system for monitoring posture and physical activity using accelerometers

Mathie, M.; Basilakis, Jim; Celler, Branko G.

doi:10.1109/iembs.2001.1019627

Cited by 105 publications

(78 citation statements)

References 10 publications

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“…In addition, on the same line Mathie et al [25] performed a survey where volunteers chose the side of the waist, above the anterior superior iliac spine, as the most suitable place to carry a small inertial sensor.…”

Section: Sensor Positionmentioning

confidence: 99%

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer

Pérez-López

Samà

Rodríguez-Martín

et al. 2016

Artificial Intelligence in Medicine

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Background: After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen.Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care.Objective: To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. Materials and methods:Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. Results:Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. Conclusion:The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.

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Section: Sensor Positionmentioning

confidence: 99%

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer

Pérez-López

Samà

Rodríguez-Martín

et al. 2016

Artificial Intelligence in Medicine

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“…A very well addressed sub-task of Health Condition is the fall detection in which accelerometers are seen as the suitable technology. [17], [11], [57], [73], [65], [55], [16], [74], [56], [130], [137], [119]; A B [82]; 2xA B [23]; G [18]; AG [64] Floor vibration-based sensor [4]; array of acoustic sensors [94]; Tilt sensor + piezoelectronic A + vibration sensor [83]; tri-axial MTx [134]; A T +G T [50], [127]; multiple A T [44]; A T +BAR [114] Kin [72], [90]; Cam [101], [7], [41] IR, pressure sensor, audio…”

Section: Physiological Health Condition and Healthcarementioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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“…Results showed an accuracy of 94.6% for a person using the training set of that person, whereas the accuracy was 56.3% using the training sets of all the other people. Another research work exploited an accelerometer placed on the waist (Mathie et al, 2001). The device was so small that it fitted in a belt.…”

Section: Wwwintechopencommentioning

confidence: 99%