A variety of clinical scales are available to assess dyskinesia severity in Parkinson's disease patients; however, such assessments are subjective, do not provide long term monitoring, and their use is subject to inter- and intra-rater variability. In this paper, an objective dyskinesia score was developed using an IMU -based motion capture system. Deep brain stimulation (DBS) surgery is currently the only acute intervention that results in the rapidly progressive reduction of dyskinesia's severity; hence, this form of therapy was selected as a model to validate the proposed method. Thirteen Parkinson's disease participants undergoing DBS surgery and 12 age-matched healthy control participants were assessed using the motion capture system. Concurrent Unified Dyskinesia Rating Scale (UDysRS) ratings were also performed. Parkinson's disease participants were assessed pre-operatively and for five visits post-operatively while seated at rest, during arms outstretched and while performing an action task. The kinematic data were used to develop an objective measure defined as the dyskinesia severity score. Generally, a strong correlation was observed between the UDysRS ratings and the full-body dyskinesia severity scores. The results suggest that it is feasible and clinically meaningful to utilize an objective full-body dyskinesia score for the assessment of dyskinesia. The portable motion capture system along with the developed software can be used remotely to monitor the full-body severity of dyskinesia, necessary for therapeutic optimization, especially in the patients home environment.
People suffering from Alzheimer's disease (AD) and their caregivers seek different approaches to cope with memory loss. Although the AD patients want to live independently, they often need help from the caregivers. In this situation, caregivers may attach notes on every single object or taking out contents of drawer to make them visible before leaving the patient alone at home. This study reports preliminary results on an Ambient Assisted Living (AAL) realtime system, achieved through Internet of Things (IoT) and Augmented Reality (AR) concepts, aimed at helping people suffering from AD. The system has two main sections: the first one is the smartphone or windows application that allows caregivers to monitor patients' status at home and be notified if patient are at risk. The second part allows patient to use smart glasses to recognize QR codes in the environment and receive information related to tags in the form of audio, text or three-dimensional image. This work presents preliminary results and investigates the possibility of implementing such a system.
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