Activity Pattern Mining for Healthcare

Cerebellar Ataxia (CA) is a neurological disease with the symptom of poor coordination of movement and balance disorders. In clinical medicine, the heel-knee-shin test and the rapid alternating movements test are important basis for assessing CA. Based on the above tests, this paper presents a non-contact method and investigates the feasibility of this method for detecting CA. This body sensor networks uses wireless devices operating in the C-band frequency range to capture data of both types of tests without intrusiveness. The obtained data of tests contains massive useful information about human health which is really significant to subjects. But the information can be so subtle that people ignore the value hidden in it. So utility pattern mining (UPM) is used for the purpose of mining subjects' activity pattern. We find that the subjects' activity pattern differs greatly in amplitude information. We extracted the amplitude information that is helpful for analyzing the test' results to determine whether the test is positive or negative. Then we use different kind of algorithms to classify the data samples. Among them, support vector machine (SVM) has the best classification effect on both tests. In the heel-knee-shin test, the coincidence rate ($\pi$ ) is 98.7%, the sensitivity (Se) is 98.9% and the specificity (Sp) is 98.5%. In the rapid alternating movements test, the $\pi $ is 99.4%, Se is 99.8% and Sp is 99%. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate CA monitoring in a patient-friendly and flexible environment.