A mobile-based system can assess Parkinson’s disease symptoms from home environments of patients
Parkinson’s disease (PD) is a neurodegenerative disorder of the central nervous system that is associated with a number of motor and non-motor symptoms. The major motor symptoms of the disease include bradykinesia (slowness of initiating voluntary movements), rigidity (increased muscle tone), tremor (a 3–5 Hz tremor at rest) and impaired postural stability. A challenge for the clinical management of the disease is the large within- and between- patient variability in symptom profiles. Furthermore, PD patients experience fluctuations in symptoms during both under- and over-medications. When under-medicated patients experience common PD symptoms whereas when over-medicated they experience abrupt, involuntary movements also known as dyskinesias. The most common way for assessing PD motor symptoms is during clinical visits by using clinical rating scales like the Unified Parkinson’ Disease Rating Scale (UPDRS) and the 39-item PD questionnaire (PDQ-39). Although these in-clinic rating scales have proved to be useful in quantifying the severity of the symptoms, their main limitation is related to the low resolution of assessments by providing a momentary snapshot of the clinical condition of the patients. In addition, the clinical visit is experimental and may not accurately represent the activities of patients in their home environments. Telemedicine methods for collecting, summarizing and visualizing symptom data can be useful in this context. In contrast to the in-clinic scales, these methods are useful for detecting subtle symptom changes as well as for providing objective (observer-independent) measures that can be repeated at multiple time points. This article presents the development and evaluation of computer- based methods for automatic and remote monitoring of PD symptoms, using data collected by means of a telemetry touch screen device. A summary of different studies and results can be found below. The summary was published in entirety as part of the doctoral thesis entitled “Mobile systems for monitoring Parkinson’s disease” at the School of Science and Technology, Örebro University1.
