In sensor-based activity recognition, most of the previous studies focused on single activities such as body posture, ambulation and simple daily activities. Few works have been done to analyze complex concurrent activities. The purpose of this paper is to use a statistical modeling approach to classify them.
In this study, the recognition problem of concurrent activities is explored with the framework of parallel hidden Markov model (PHMM), where two basic HMMs are used to model the upper limb movements and lower limb states, respectively. Statistical time-domain and frequency-domain features are extracted, and then processed by the principal component analysis method for classification. To recognize specific concurrent activities, PHMM merges the information (by combining probabilities) from both channels to make the final decision.
Four studies are investigated to validate the effectiveness of the proposed method. The results show that PHMM can classify 12 daily concurrent activities with an average recognition rate of 93.2 per cent, which is superior to regular HMM and several single-frame classification approaches.
A statistical modeling approach based on PHMM is investigated, and it proved to be effective in concurrent activity recognition. This might provide more accurate feedback on people’s behaviors.
The research may be significant in the field of pervasive healthcare, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.
This work was supported by National Natural Science Foundation of China under Grant No. 61473058 and Grant No. 61174027, and Fundamental Research Funds for the Central Universities (DUT15ZD114).
Wang, Z. and Chen, Y. (2017), "Recognizing human concurrent activities using wearable sensors: a statistical modeling approach based on parallel HMM", Sensor Review, Vol. 37 No. 3, pp. 330-337. https://doi.org/10.1108/SR-01-2017-0003
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