Abstract
As the number of elderly people has increased worldwide, there has been a surge of research into assistive technologies to provide them with better care by recognizing their normal and abnormal activities. However, existing abnormal activity recognition (AAR) algorithms rarely consider sub-activity relations when recognizing abnormal activities. This paper presents an application of the Hidden State Conditional Random Field (HCRF) method to detect and assess abnormal activities that often occur in elderly persons’ homes. Based on HCRF, this paper designs two AAR algorithms, and validates them by comparing them with a feature vector distance based algorithm in two experiments. The results demonstrate that the proposed algorithms favorably outperform the competitor, especially when abnormal activities have same sensor type and sensor number as normal activities.
Highlights
Smart homes are one realization of ambient intelligence (AmI) [1] which is emerging as an omnipresent computing technology that can anticipate people’s goals and intentions with contextual sensor data
In 2011, Jakkula et al [14] proposed recognizing abnormal activities based on a One-class Support Vector Machine (One-class SVM), and later Lotfi et al [15] proposed recognizing abnormal activities in smart homes based on a clustering and neural networks method, but neither both of them can model the temporal relationships between activities
Hidden State Conditional Random Field (HCRF) one can model the similarity between a testing sample and training labels, so we proposed to recognize abnormal activities based on HCRF, and use a likelihood vector to represent the similarity between the testing activity and every type of training activity, using the maximum likelihood value (MLV) index to represent the most similar training activity, and using MLV to represent the similar value between a testing activity and the most similar training activity
Summary
Smart homes are one realization of ambient intelligence (AmI) [1] which is emerging as an omnipresent computing technology that can anticipate people’s goals and intentions with contextual sensor data. Abnormal activity recognition (AAR) in smart home is an emerging technology that can help elderly residents live comfortably and safely by identifying unexpected and irregular events [5], e.g., falls or taking medicine many times in a short time. In 2007 and 2008, Jakkula et al [12,13] proposed recognizing abnormal activities using non-obtrusive sensors In their research, they used temporal logic (before, after, meets, overlaps, starts ...) to identify temporal relationships between events and detected anomalies by calculating the probability of a given event occurring or not occurring. HCRF is a sequence probabilistic graphical model which introduces probability calculus and statistical inference, and takes root in MEM It can capture internal substructures and model context relations of sub-activities by detecting causal dependencies from data.
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