Introduction & Aims
People with Parkinson's disease (PwP) experience motor symptom fluctuations, which reflect broader disease severity fluctuations. The cardinal symptom triad includes slowed movement, rigidity, and tremor. Parkinson’s disease (PD) symptom monitoring currently relies on sparse, subjective in-clinic assessments with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and patient-reported symptom diaries, leading to suboptimal optimisation of treatment regimens. Patients typically take medication which replaces dopamine function, however, these medications wear off during the day. ON refers to periods when symptoms are well controlled by medication, and OFF refers to the inverse. We propose a minimally intrusive system for continuous, objective (home )monitoring of PD using wireless radar sensors.
 
Method(s)
The UK Dementia Research Institute Care Research & Technology Centre (DRI CR&T) is home to the Living Lab—a sensor-rich studio apartment-style laboratory where healthy adults and people with Parkinson’s disease (PwP) are invited to partake in a series of experiments [1]. Ethical approvals have been granted by the Imperial College Research Ethics Committee (reference number: 21IC6992). 7 healthy older adults and 7 PwP taking PD medication were invited to partake in the DRI Living Lab study. PwP are asked to repeat the protocol twice, 2–3 hours apart, to capture ON and OFF states. Gross motion (including gait) was observed during walking tasks and free motion. Statistical tests including the Wilcoxon signed-rank test were conducted.
Results & Conclusion(s)
Experiments conducted at the Dementia Research Institute's Living Lab demonstrate this system's ability to monitor clinically relevant motion parameters related to PD gait. Specifically, radar-derived gait velocity and smoothness of motion measures distinguished PD patients from healthy elderly controls and detected intra-day fluctuations in PD motor function corresponding to ON/OFF medication states. This contactless system operates passively in the background without requiring patient interaction or video recording, thereby maintaining privacy. Furthermore, it obviates the need for wearable devices and similar methods which place a burden on patients. Ongoing in-home deployments will evaluate the system's suitability for longitudinal PD monitoring and its capacity to holistically track parameters including sleep, vital signs, and location concurrently with motor symptoms. Overall, this work demonstrates a promising approach to enable frequent, quantitative, and objective evaluations of PD symptom severity in natural home environments, with the potential to improve treatment decision-making and by proxy, patient outcomes.