Objective Measurement of Walking Activity Using Wearable Technologies in People with Parkinson Disease: A Systematic Review Protocol

Introduction Parkinson's disease (PD) is a complex neurodegenerative disease with motor and nonmotor symptoms with a multitude of disease variations and severity. Physical activity can improve the management of disease symptoms and increase patients' quality of life. Technological development of small wearable devices allows objective activity measurement such as daily step count. Objective To synthesize ongoing and past research on objective walking activity measurements using wearable devices in patients with PD. Methods PubMed, Cochrane, Web of Science, and PEDro database are systematically searched with no limitation on publication date. Keywords are relative to (1) the population, (2) the measurement tool, and (3) the measured outcomes. Only full-text English articles published in a peer-reviewed journal will be included. Participants do not have to undergo any type of intervention. Included studies must report an objective measurement of walking activity using wearable devices in PD patients. After an independent screening process done by 2 reviewers, data will be extracted from the articles according to the following 5 set of data: (1) the study metrics, (2) the population characteristics, (3) the measurement tools, (4) the experimental procedure, and (5) the reported outcomes. Results The results will contain inter alia summaries of the wearables' specifications, wearing location, and recommendations for feasible methodologies to capture daily walking activity. Discussion This review aims to synthesize the evidence of objective walking activity assessment with wearable devices in patients with PD. It will also provide recommendations with regard to device selection and suggest key points when monitoring walking activity in this specific population

EFFECT OF SYSTEMATIC LANDING TRAINING ON KNEE KINEMATICS AND GROUND REACTION FORCES IN YOUNG ADULTS

In gymnastics, the final landing position represents a key determinant of safety and exercise quality. Previous findings on the biomechanics of landing indicated that knee flexion correlates strongly with ground reaction forces. However, it remains unclear how this relationship is affected by landing training. We conducted a randomized controlled study to assess the effect of systematic landing training on knee kinematics and ground reaction forces in young adult beginner gymnasts. The study included three-dimensional motion analysis of knee flexion and measurement of ground reaction forces for landings from heights of 37 and 87cm. Of the 28 beginner gymnasts who participated in the study, 14 underwent five weeks of landing training, whereas 14 served as controls (no intervention). A significant pre-post difference (-11.2°) was observed only for the control group, and only regarding maximum knee flexion after landings from heights of 37cm. Although no significant effects were noted overall for the training group, systematic landing training seems effective for correcting those landings that deviated strongly from the target position prior to training initiation (37cm, r=-0.74; 8cm, r=-0.77; both with p< 0.01). Thus, while landing training appears to minimize peak forces at ground contact, our findings cannot be explained solely in terms of knee kinematics, warranting muscle activity analysis.  Article visualizations

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As align ment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥ 92%, precision ≥ 97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases

Comparing Backward Walking Performance in Parkinson’s Disease with and without Freezing of Gait—A Systematic Review

Introduction: Parkinson’s disease (PD) is a neurodegenerative disease characterized by motor symptoms and gait impairments. Among them, freezing of gait (FOG) is one of the most disabling manifestations. Backward walking (BW) is an activity of daily life that individuals with PD might find difficult and could cause falls. Recent studies have reported that gait impairments in PD were more pronounced in BW, particularly in people presenting FOG. However, to the best of our knowledge, no systematic review has synthetized the literature which compared BW performance in PD patients with and without FOG. Objective: The aim of this study was to evaluate the differences in BW performance between PD patients with FOG and PD patients without FOG. Methods: Two databases, PubMed and Web of Science, were systematically searched to identify studies comparing BW performance in PD patients with and without FOG. The National Institutes of Health (NIH) tool was used to assess the quality of the studies included. Results: Seven studies with 431 PD patients (179 PD with FOG and 252 PD without FOG) met the inclusion criteria and were included in this review. Among them, 5 studies reported walking speed, 3 studies step length, stride length and lower limb range of motion, 2 studies functional ambulation profile, toe clearance height, swing, and stance percent and 1 study reported the decomposition index and stepping coordination. Compared to PD patients without FOG, PD patients with FOG showed slower walking speed and reduced step length in 3 studies, shorter stride length, lower functional ambulation profile and decreased ankle range of motion in 2 studies, and smaller swing percent, higher stance percent, worse stepping coordination, greater decomposition between movements, and lower toe clearance height in one study. Conclusion: Despite the small number of included studies, the findings of this review suggested that PD patients with FOG have worse gait performance during the BW task than PD without FOG

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (-0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases

INERTIAL MEASUREMENT UNIT IN BIOMECHANICS AND SPORT BIOMECHANICS: PAST, PRESENT, FUTURE

The current technologies and methodologies used for physical activity monitoring and ambulatory motion analysis are based on the Inertial Measurement Unit (IMU). Perspectives and issues met with when performing physical activity monitoring and ambulatory motion analyses with this type of device are presented here

Active Magnetoelectric Motion Sensing: Examining Performance Metrics with an Experimental Setup

Magnetoelectric (ME) sensors with a form factor of a few millimeters offer a comparatively low magnetic noise density of a few pT/Hz−−−√ in a narrow frequency band near the first bending mode. While a high resonance frequency (kHz range) and limited bandwidth present a challenge to biomagnetic measurements, they can potentially be exploited in indirect sensing of non-magnetic quantities, where artificial magnetic sources are applicable. In this paper, we present the novel concept of an active magnetic motion sensing system optimized for ME sensors. Based on the signal chain, we investigated and quantified key drivers of the signal-to-noise ratio (SNR), which is closely related to sensor noise and bandwidth. These considerations were demonstrated by corresponding measurements in a simplified one-dimensional motion setup. Accordingly, we introduced a customized filter structure that enables a flexible bandwidth selection as well as a frequency-based separation of multiple artificial sources. Both design goals target the prospective application of ME sensors in medical movement analysis, where a multitude of distributed sensors and sources might be applied

Quantification of Arm Swing during Walking in Healthy Adults and Parkinson's Disease Patients: Wearable Sensor-Based Algorithm Development and Validation

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease

A Complementary Filter Design on SE(3) to IdentifyMicro-Motions during 3D Motion Tracking

In 3D motion capture, multiple methods have been developed in order to optimize thequality of the captured data. While certain technologies, such as inertial measurement units (IMU),are mostly suitable for 3D orientation estimation at relatively high frequencies, other technologies,such as marker-based motion capture, are more suitable for 3D position estimations at a lower frequencyrange. In this work, we introduce a complementary filter that complements 3D motion capture datawith high-frequency acceleration signals from an IMU. While the local optimization reduces the error ofthe motion tracking, the additional accelerations can help to detect micro-motions that are useful whendealing with high-frequency human motions or robotic applications. The combination of high-frequencyaccelerometers improves the accuracy of the data and helps to overcome limitations in motion capturewhen micro-motions are not traceable with 3D motion tracking system. In our experimental evaluation,we demonstrate the improvements of the motion capture results during translational, rotational,and combined movements

Scoring the Sit-to-Stand Performance of Parkinson's Patients with a Single Wearable Sensor

Monitoring disease progression in Parkinson's disease is challenging. Postural transfers by sit-to-stand motions are adapted to trace the motor performance of subjects. Wearable sensors such as inertial measurement units allow for monitoring motion performance. We propose quantifying the sit-to-stand performance based on two scores compiling kinematics, dynamics, and energy-related variables. Three groups participated in this research: asymptomatic young participants (n = 33), senior asymptomatic participants (n = 17), and Parkinson's patients (n = 20). An unsupervised classification was performed of the two scores to differentiate the three populations. We found a sensitivity of 0.4 and a specificity of 0.96 to distinguish Parkinson's patients from asymptomatic subjects. In addition, seven Parkinson's patients performed the sit-to-stand task "ON" and "OFF" medication, and we noted the scores improved with the patients' medication states (MDS-UPDRS III scores). Our investigation revealed that Parkinson's patients demonstrate a wide spectrum of mobility variations, and while one inertial measurement unit can quantify the sit-to-stand performance, differentiating between PD patients and healthy adults and distinguishing between "ON" and "OFF" periods in PD patients is still challenging