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Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

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Human activity recognition (HAR) is an emerging methodology essential for smart homes with practical applications such as personal lifecare and healthcare services for the elderly and disabled people. In this work, we present a novel HAR methodology utilizing the recognized body parts of human depth silhouettes and Hidden Markov Models (HMMs). We first create a database of synthetic depth silhouettes and their corresponding body parts labelled silhouettes of various human activities to train random forests (RFs). With the trained RFs, a set of 23 body parts are recognized from incoming depth silhouettes, yielding a set of centroids from the identified body parts. From the dynamics of these centroids, motion parameters are computed: a set of magnitude and directional angle features. Finally, the spatio-temporal dynamics of these motion features of various activities are used to train HMMs. We have performed HAR with the trained HMMs for six typical home activities and obtained the mean recognition rate of 97.16%. The presented HAR methodology should be useful for residents monitoring services at smart homes.