access icon free Unsupervised posture detection by smartphone accelerometer

Proposed is a light-weight unsupervised decision tree based classification method to detect the user's postural actions, such as sitting, standing, walking and running as user states by analysing the data from a smartphone accelerometer sensor. The proposed method differs from other approaches by applying a sufficient number of signal processing features to exploit the sensory data without knowing any a priori information. Experiments show that the proposed method still makes a solid differentiation in user states (e.g. an above 90% overall accuracy) even when the sensor is operated under slower sampling frequencies.

Inspec keywords: decision trees; sensors; accelerometers; smart phones; signal sampling

Other keywords: classification method; walking; unsupervised posture detection; sitting; data sensor; running; sampling frequency; standing; light-weight unsupervised decision tree; data analysis; smartphone accelerometer sensor; signal processing

Subjects: Velocity, acceleration and rotation measurement; Combinatorial mathematics; Velocity, acceleration and rotation measurement; Sensing devices and transducers; Sensing and detecting devices

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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2013.0592
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