Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling

“Smart Home” services offer to improve living conditions and levels of independence for the elderly that require support with both physical and cognitive functions via Activities of Daily Living (ADL). Due to human ethics and privacy concern, ambient-based sensor technologies are preferred and deployed in the environment. Nevertheless, as human activities gradually becoming complex and thus complicate the inferences of activities especially involving multi-resident within the same home premises that deploy solely ambient-based sensor technology. Existing works and solutions focused on separate models for recognizing the residents, activities and interactions. On top of that, data association and algorithm modification inherit drawbacks on recognizing the residents and interactions of multi-resident complex activities. When the data are induced with the lower quality model, the performance is also truncated. Furthermore, there is tendency that multi label classifications used instead of traditional single label classification technique. Consequently, this could cater the simple and complex activity recognition of multi-resident in a separate model. Moreover, with the incremental numbers of resident living together in the same smart home environment, the class-overlapping sensor event sequence could occur and might share the same features for subsequences that correspond to each individual activity. At the same time, the sensor events are always uncertain and intricate in nature led to conflict occurs at its interaction layer. In accordance to the mentioned problem, Label Combination (LC) of multi label classification is introduced because of its ability to transform the multi label problem into 2ᶫ multi-class problem and exploit the correlation between the class labels. On top of that, the label correlation can be solved with the Random Forest (RF) as a base classifier due to its capability to produce the most probable class from its majority-voting task as output. Nevertheless, the learning complexity of classification is increased due to the increment number of residences and activities are also intricate. Therefore, Adaptive Profiling (AP) for multi-resident involving context information includes temporal and spatial information is proposed to address the class-overlapping using Expectation-Maximization (EM) clustering. The clusters parameter is adaptively generated from the active labelset from the real-world data. The multi label relation method using Two-Stage Label Construction (TSLC) is presented, resolve the conflicts in complex activity of multi-resident is also outlined in this research. Two publicly available datasets; WSU’s CASAS and ARAS Dataset are selected and experimented to evaluate the proposed framework. About 26 pairs of volunteer performing 15 scripted activities collected over four months’ time with almost 17,500 instances from CASAS. In addition, three days of house A from ARAS dataset is also selected to evaluate its effectiveness. LC-RF is tested with other base classifiers such as k-NN, SVM and HMM. However, LC-RF showed the most promising results among others. Furthermore, its performance is also benchmarked with previous work that used single label classification. Consequently, the obtained results demonstrate the improvement of 2.4% increment in Hamming score as compare with the highest results from the previous work. Experimental results have significantly promised an improvement level in multi-resident simple and complex activity recognition simultaneously, capable to cater the problems mentioned specifically when the number of resident increase and reside together in the same smart home environment.

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