Rule-based approach for enhancing the motion trajectories in human activity recognition

Hassan, Sherien Mohamed; Al-Sadek, Ahmed Farouk; Hemayed, Elsayed E.

doi:10.1109/isda.2010.5687161

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…For example, the Color and Edge Directivity Descriptor (CEDD) [10] combines color and edge distributions for static image indexing. The scale-invariant feature transform (SIFT) [11] is one of the most utilized features applied to activity recognition [12–14]. There are also features specifically designed for motion sensors (accelerometer or gyroscope) for physical activity recognition such as the Signal-Magnitude Area (SMA) [3], Accumulated Height (AH), Autoregressive Coefficients (AC), Time Between Peaks (TBP) and Binned Distribution (BD) [8].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Hidden Markov Model for Activity Recognition Based on a Wearable Multi-Sensor Device

Wei

Yue

et al. 2015

J Med Syst

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Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.

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