2012
DOI: 10.3390/s121013185
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Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer

Abstract: In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human mo… Show more

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Cited by 7 publications
(2 citation statements)
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References 26 publications
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“…Medical or consumer wearable devices have potential to monitor biometrics for antecedent ‘signs’ of SCA, which may be transformational since many arrests are unwitnessed. Sensors could include ECG electrodes, photoplethysmography on limbs or the face (18) to detect pulse, or motion sensors (19) to detect lack of breathing or a sudden collapse. Sensors in smart houses (20), on smart mattresses (21) embedded in garment fabrics (22) or in other locations could network with portable devices to create an internet of things (IoT) for SCA.…”
Section: Respondmentioning
confidence: 99%
“…Medical or consumer wearable devices have potential to monitor biometrics for antecedent ‘signs’ of SCA, which may be transformational since many arrests are unwitnessed. Sensors could include ECG electrodes, photoplethysmography on limbs or the face (18) to detect pulse, or motion sensors (19) to detect lack of breathing or a sudden collapse. Sensors in smart houses (20), on smart mattresses (21) embedded in garment fabrics (22) or in other locations could network with portable devices to create an internet of things (IoT) for SCA.…”
Section: Respondmentioning
confidence: 99%
“…The smartphone opportunistic sensor approach is on the rise to integrate physical sensors with the context [14]. Ahmed and Song developed and demonstrated a Bayesian method for human motion classification, which includes the learning process of new classes of motion unknown to the system, based on the utilisation of smartphone accelerometers, [15]. Performance of four machine learning methods in resolution of the problem of the human motion class identification, based on bespoke inertial sensors set on human limbs (i.e.…”
mentioning
confidence: 99%