Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study

Abstract

Background Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders. Trial Registration ClinicalTrials.gov NCT03366558; https:/...

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

“…Studies dedicated to clinical research use IMUs as a quantification tool for the study of specific cohorts [ 66 , 67 , 68 ] and constitute the second category (24 studies). Some of those clinical studies aim at quantifying patient’s pathology [ 6 , 7 , 27 , 69 , 70 ], while others quantify the therapeutic effect on the patient’s condition [ 5 , 68 ]. Some clinical trials focus on healthy participants, some compare the patients included with healthy participants [ 39 , 42 , 71 , 72 ], while others dwell specifically upon cohorts with a medical condition.…”

“…Some recent studies use smartphones to retrieve details of the same parameters measured by the other types of motion sensors mentioned above. Three of these studies are included in this review [ 5 , 67 , 83 ].…”

“…This highlights once again the importance of being able to assess the level of PA of certain populations, particularly in environments where their daily activities take place. Some studies are particularly oriented towards the study of cohorts affected by pathologies impacting this PA such as neurological diseases (Parkinson’s Disease (PD) [ 5 ] and multiple sclerosis), Chronic Obstructive Pulmonary Disease (COPD) [ 6 , 7 ], or traumas such as strokes.…”

“…These sensors can be used to estimate the static position of the body [ 13 ] as well as to detect [ 14 ] and study certain movements, particularly in open environments. Gyroscopes measure the angular velocities in one or more directions of space at a given point [ 5 , 15 , 16 ]. The latter can be used to compute specific features such as angles between different parts of the body [ 17 , 18 ] or Range of Motion (ROM) of the trunk to assess the stability of a subject’s gait [ 19 ].…”

“…The increasing use of machine learning and efficient algorithms also contributed to this effort, by allowing the processing of complex data and allowing to transpose protocols from laboratory to natural conditions. Indeed, although some studies are focused on Semi-Free-Living Environments (semi-FLEs) [ 15 , 41 , 42 , 43 ] where the conditions are controlled by the experimenter, more and more research works are being dedicated to Free-Living Environments (FLEs) which use the subjects’ natural environment [ 5 , 6 , 44 ].…”