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The proliferation of smartphones has significantly facilitated people's daily life, and diverse and powerful embedded sensors make smartphone a ubiquitous platform to acquire and analyze data, which may also provide great potential for efficient human activity recognition. This paper presents a systematic performance analysis of motion-sensor behavior for human activity recognition via smartphones. Sensory data sequences are collected via smartphones, when participants perform typical and daily human activities. A cycle detection algorithm is applied to segment the data sequence for obtaining the activity unit, which is then characterized by time-, frequency-, and wavelet-domain features. Then both personalized and generalized model using diverse classification algorithms are developed and implemented to perform activity recognition. Analyses are conducted using 27 681 sensory samples from 10 subjects, and the performance is measured in the form of F-score under various placement settings, and in terms of sensitivity to user space, stability to combination of motion sensors, and impact of data imbalance. Extensive results show that each individual has its own specific and discriminative movement patterns, and the F-score for personalized model and generalized model can reach 95.95% and 96.26%, respectively, which indicates our approach is accurate and efficient for practical implementation.
Chen et al. (Sun,) studied this question.