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The symptoms of (PD) often begins on one side of the body before separating bilaterally. Early symptoms of PD are rest tremors, bradykinesia and rigidity. Common signs are a decrease in arm swing, shuffling foot movement and slowness arising from a chair. Rigidity of muscles commonly affects a patient limbs, neck and shoulder PD is currently diagnosed after the neuro degenerative process has started. To optimize and improve PD quality of care, it should be diagnosed early in its onset once symptoms are not yet evident. The current gold standard for patient assessment of PD is the completion of symptom diaries. These diaries are subjective and usually do not accurately reflect what is taking place throughout the day. It is exceedingly difficult for an untrained observer such as family members to provide accurate description of movement characteristic in PD. An alternative way to exercise the abnormal movement in PD is to use a wearable technology system which could aid early detection of the disease and help clinicians manage medication when symptoms fluctuate throughout the day. This paper will evaluate the effectiveness of a wearable multi-sensor kinematic system capable of detecting the tell-tale signs of PD when performing tailored exercise routines. Each exercise routine will examine movement for potential PD symptoms and report finding for deeper clinician analysis.

Wearable sensor technology is often used in healthcare environments for monitoring, diagnosis and recovery of patients. Wearable sensors can be used to detect movement throughout measurement of standardized functional tests, which are considered part of the assessment criteria for Activities of Daily Living (ADL). The volume of data collected by sensors for long term assessment of ambulatory movement can be very large in tuple size since they may contain detailed 3-D sensor information. Extracting recorded movement data corresponding to standardized functional tests from an entire data set is complex and time consuming. This paper examines whether standardized functional tests can be automatically detected from long term data collected by wearable technology devices using Artificial Intelligence (AI) techniques. The current research work is aligned with clinical trial data generated by patients who are suffering from Axial Spondylo Arthritis (axSpA). These datasets contain Inertial Measurement Unit (IMU) values corresponding to individual patient functional tests for axSpA. Rotation angles with respect to each functional test are plotted against time. Individual movements that form part of a functional test are constructed for training and testing the AI system. Individual movement patterns are split into training and testing data inputs and are used to train the Neural Network (NN) system and to estimate overall prediction accuracy of the NN system. NN model is trained in such a way that the learned system can predict new functional test patterns with respect to the trained data and it is compared with expected data set and returned the accuracy of prediction. Once the semi supervised learning phase of AI system has successfully finished with adequate amount of data, it is capable for automatically detect gait and posture changes of patients at home.