Body-Worn Sensors for Remote Monitoring of Parkinson’s Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead

Abstract

The increasing prevalence of neurodegenerative conditions such as Parkinson’s disease (PD) and related mobility issues places a serious burden on healthcare systems. The COVID-19 pandemic has reinforced the urgent need for better tools to manage chronic conditions remotely, as regular access to clinics may be problematic. Digital health technology in the form of remote monitoring with body-worn sensors offers significant opportunities for transforming research and revolutionizing the clinical management of PD. Significant efforts are being invested in the development and validation of digital outcomes to support diagnosis and track motor and mobility impairments “off-line”. Imagine being able to remotely assess your patient, understand how well they are functioning, evaluate the impact of any recent medication/intervention, and identify the need for urgent follow-up because things are changing? This could offer new pragmatic solutions for personalized care and clinical research. So the question remains: how close are we to achieving this? Here, we describe the state-of-the-art based on representative papers published between 2017 and 2020. We focus on remote (i.e., real-world, daily-living) monitoring of PD using body-worn sensors (e.g., accelerometers, inertial measurement units) for assessing motor symptoms and their complications. Despite the tremendous potential, existing challenges exist (e.g., validity, regulatory) that are preventing the widespread clinical adoption of body-worn sensors as a digital outcome. We propose a roadmap with clear recommendations for addressing these challenges and future directions to bring us closer to the implementation and widespread adoption of this important way of improving the clinical care, evaluation, and monitoring of PD.

The state of telemedicine for persons with Parkinson's disease

“…Telemonitoring is the remote gathering of information about a patient which is used to inform healthcare providers (in a clinical setting) or researchers (in the framework of a trial). A wide and expanding spectrum of tools can be used for telemonitoring, including body-worn sensors [ 27 , 28 ], home sensors [ 29 ], specific apps for the smartphone [ 30 , 31 ], digital diaries [ 32 ], or analysis of common appliances such as computer keyboards [ 33 ] (only several selected high-quality references are given here). The promise of remote monitoring is to offer objective, continuous measures of relevant symptoms while patients are at home.…”

Detection of Unsupervised Standardized Gait Tests From Real-World Inertial Sensor Data in Parkinson’s Disease

“…2) Parameter Optimization: The cost threshold Θ and the turns threshold range [η min , η max ] were tunable parameters [0,24], [2,22], [4,20], [6,18], [8,16], [10,14], [11,13] and optimized using a grid search. For the turns threshold range, symmetric interval borders around the expected amount of twelve detected turnings were used as parameter options for the grid search (Table II).…”

“…Sensor-based gait analysis using wearable inertial measurement units (IMUs) has increasingly been used in clinical settings [5] and long-term monitoring in the real world [6] to provide complementary objective information on movement impairments. The accuracy of sensor-based gait parameter estimations has been validated for patients with movement impairments [7], [8].…”

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

“…Similarly, Igual et al [ 24 ] talks about the issues and trends in fall detection schemes. The study [ 25 ] is specific to fall detection using wearable sensors. All the above-mentioned reviews only discuss fall detection schemes with no interest in fall prevention.…”