HMM machine learning and inference for Activities of Daily Living recognition

Authors:
Bo-Chao Cheng;Yi-An Tsai;Guo-Tan Liao;Eui-Seok Byeon
Affiliations:
Department of Communications Engineering, National Chung Cheng University, Chia-Yi, Taiwan 621;Department of Communications Engineering, National Chung Cheng University, Chia-Yi, Taiwan 621;Department of Communications Engineering, National Chung Cheng University, Chia-Yi, Taiwan 621;Department of Industrial and Management Engineering, Sun Moon University, Asan-Si, Korea 336-740
Venue:
The Journal of Supercomputing
Year:
2010

Citing 5
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Abstract

In a human-centric smart space, Activities of Daily Living (ADL) analysis can provide very useful information for elder care and long-term care services. ADL is defined as an assessment of a person's functional status. Many recent researches concentrate on designing a good Context Aware Computing System to automate the actions necessarily triggered by ADL recognitions. Implementing a correct ADL recognition engine is a hard work, but will repay the system with lower inference errors and higher system dependability. A good ADL recognition engine is required to adjust its inference strategy based on the learning capability in order to avoid a high error rate, especially in real world inputs with a significant difference as compared to those in the training phase. In this paper, we proposed a powerful inference engine based on the Hidden Markov Model, called the Adaptive Learning Hidden Markov Model (ALHMM), which combines the Viterbi and Baum---Welch algorithms to enhance the accuracy and learning capability. The assessments of ALHMM are conducted on the Python platform and show the practical feasibility of Activity Recognition in residential homes. Such a technique can provide the key answer required for advancing the state-of-the-art in context-aware computing and applications in real life.