A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data.

Fatima Amjad,Marcin Grzegorzek,Muhammad Shahid Farid,Muhammad Hassan Khan,Muhammad Adeel Nisar

doi:10.3390/s21072368

Abstract

Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

Highlights

This paper presents a novel two-level hierarchical method to recognize human activities using a set of wearable sensors
We propose a two-level hierarchical model which detects atomic activities at the first level using raw sensory data obtained from multiple wearable sensors, and later the composite activities are identified using the recognition score of atomic activities at the second level
A two-level hierarchical technique is proposed to recognize human activities using a set of wearable sensors

Summary

Introduction

The main objective of such a system is to automatically detect and recognize the human daily life activities from the captured data by creating a predictive model that allows the classification of an individual’s behavior [1]. We can describe the long term activities as composite activities, e.g., cooking, playing, etc., whereas the small sequence of actions are known as atomic activities, such as raising an arm or a leg [3]. Numerous existing techniques focused on identifying the simple and basic human actions, whereas recognizing the composite activities remains an active problem. This paper summarizes the existing piece of work on hierarchical activity recognition techniques to encode the temporal patterns of composite activities. The techniques proposed in References [17,18] have concluded that the hierarchical recognition models are effective to recognize human activities. The technique in Reference [20]

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