scholarly journals Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2661
Author(s):  
Cameron Diep ◽  
Johanna O’Day ◽  
Yasmine Kehnemouyi ◽  
Gary Burnett ◽  
Helen Bronte-Stewart
Keyword(s):  
Center Of Pressure ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Area Under The Curve ◽  
Measurement Unit ◽  
Visual Identification ◽  
Gait Parameters ◽  
Time Coefficient ◽  
Place Task ◽  
Stepping In Place

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.

scholarly journals Gait Parameters Measured From Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

2021 ◽  
Author(s):  
Cameron Diep ◽  
Johanna O'Day ◽  
Yasmine Kehnemouyi ◽  
Gary Burnett ◽  
Helen Bronte-Stewart
Keyword(s):  
Center Of Pressure ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Measurement Unit ◽  
Kinematic Data ◽  
Visual Identification ◽  
Gait Parameters ◽  
Time Coefficient ◽  
Place Task ◽  
Stepping In Place

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (AUC = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.

scholarly journals Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramon J. Boekesteijn ◽  
José M. H. Smolders ◽  
Vincent J. J. F. Busch ◽  
Alexander C. H. Geurts ◽  
Katrijn Smulders
Keyword(s):  
Dual Task ◽  
Wearable Sensors ◽  
Body Motion ◽  
Effect Sizes ◽  
Upper Body ◽  
Healthy Controls ◽  
Gait Parameters ◽  
Dual Task Cost ◽  
Dual Task Performance ◽  
Spatiotemporal Parameters

Abstract Background Although it is well-established that osteoarthritis (OA) impairs daily-life gait, objective gait assessments are not part of routine clinical evaluation. Wearable inertial sensors provide an easily accessible and fast way to routinely evaluate gait quality in clinical settings. However, during these assessments, more complex and meaningful aspects of daily-life gait, including turning, dual-task performance, and upper body motion, are often overlooked. The aim of this study was therefore to investigate turning, dual-task performance, and upper body motion in individuals with knee or hip OA in addition to more commonly assessed spatiotemporal gait parameters using wearable sensors. Methods Gait was compared between individuals with unilateral knee (n = 25) or hip OA (n = 26) scheduled for joint replacement, and healthy controls (n = 27). For 2 min, participants walked back and forth along a 6-m trajectory making 180° turns, with and without a secondary cognitive task. Gait parameters were collected using 4 inertial measurement units on the feet and trunk. To test if dual-task gait, turning, and upper body motion had added value above spatiotemporal parameters, a factor analysis was conducted. Effect sizes were computed as standardized mean difference between OA groups and healthy controls to identify parameters from these gait domains that were sensitive to knee or hip OA. Results Four independent domains of gait were obtained: speed-spatial, speed-temporal, dual-task cost, and upper body motion. Turning parameters constituted a gait domain together with cadence. From the domains that were obtained, stride length (speed-spatial) and cadence (speed-temporal) had the strongest effect sizes for both knee and hip OA. Upper body motion (lumbar sagittal range of motion), showed a strong effect size when comparing hip OA with healthy controls. Parameters reflecting dual-task cost were not sensitive to knee or hip OA. Conclusions Besides more commonly reported spatiotemporal parameters, only upper body motion provided non-redundant and sensitive parameters representing gait adaptations in individuals with hip OA. Turning parameters were sensitive to knee and hip OA, but were not independent from speed-related gait parameters. Dual-task parameters had limited additional value for evaluating gait in knee and hip OA, although dual-task cost constituted a separate gait domain. Future steps should include testing responsiveness of these gait domains to interventions aiming to improve mobility.

scholarly journals Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2246
Author(s):  
Scott Pardoel ◽  
Gaurav Shalin ◽  
Julie Nantel ◽  
Edward D. Lemaire ◽  
Jonathan Kofman
Keyword(s):  
Early Detection ◽  
Plantar Pressure ◽  
Inertial Measurement Unit ◽  
Binary Classification ◽  
Freezing Of Gait ◽  
Pressure Sensors ◽  
Measurement Unit ◽  
Distribution Data ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

scholarly journals An Inertial Measurement Unit-Based Wireless System for Shoulder Motion Assessment in Patients with Cervical Spinal Cord Injury: A Validation Pilot Study in a Clinical Setting

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1057
Author(s):  
Riccardo Bravi ◽  
Stefano Caputo ◽  
Sara Jayousi ◽  
Alessio Martinelli ◽  
Lorenzo Biotti ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Clinical Setting ◽  
Cervical Spinal Cord ◽  
Wearable Sensors ◽  
Cervical Spinal Cord Injury ◽  
Measurement Unit ◽  
Innovative Technology ◽  
Inertial Measurement ◽  
Cord Injury

Residual motion of upper limbs in individuals who experienced cervical spinal cord injury (CSCI) is vital to achieve functional independence. Several interventions were developed to restore shoulder range of motion (ROM) in CSCI patients. However, shoulder ROM assessment in clinical practice is commonly limited to use of a simple goniometer. Conventional goniometric measurements are operator-dependent and require significant time and effort. Therefore, innovative technology for supporting medical personnel in objectively and reliably measuring the efficacy of treatments for shoulder ROM in CSCI patients would be extremely desirable. This study evaluated the validity of a customized wireless wearable sensors (Inertial Measurement Units—IMUs) system for shoulder ROM assessment in CSCI patients in clinical setting. Eight CSCI patients and eight healthy controls performed four shoulder movements (forward flexion, abduction, and internal and external rotation) with dominant arm. Every movement was evaluated with a goniometer by different testers and with the IMU system at the same time. Validity was evaluated by comparing IMUs and goniometer measurements using Intraclass Correlation Coefficient (ICC) and Limits of Agreement (LOA). inter-tester reliability of IMUs and goniometer measurements was also investigated. Preliminary results provide essential information on the accuracy of the proposed wireless wearable sensors system in acquiring objective measurements of the shoulder movements in CSCI patients.

The Feasibility and Effectiveness of Wearable Sensor Technology in the Management of Elderly Diabetics with Foot Ulcer Remission: A Proof-Of-Concept Pilot Study with Six Cases

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
Chenzhen Du ◽  
Hongyan Wang ◽  
Heming Chen ◽  
Xiaoyun Fan ◽  
Dongliang Liu ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Stride Length ◽  
Wearable Sensors ◽  
Foot Ulcer ◽  
Wearable Devices ◽  
Foot Ulcers ◽  
Diabetic Patients ◽  
Wearable Sensor ◽  
Double Support ◽  
Gait Parameters

Aims: Using specials wearable sensors, we explored changes in gait and balance parameters, over time, in elderly patients at high risk of diabetic foot, wearing different types of footwear. This assessed the relationship between gait and balance changes in elderly diabetic patients and the development of foot ulcers, in a bid to uncover potential benefits of wearable devices in the prognosis and management of the aforementioned complication. Methods: A wearable sensor-based monitoring system was used in middle-elderly patients with diabetes who recently recovered from neuropathic plantar foot ulcers. A total of 6 patients (age range: 55–80 years) were divided into 2 groups: the therapeutic footwear group (n = 3) and the regular footwear (n = 3) group. All subjects were assessed for gait and balance throughout the study period. Walking ability and gait pattern were assessed by allowing participants to walk normally for 1 min at habitual speed. The balance assessment program incorporated the “feet together” standing test and the instrumented modified Clinical Test of Sensory Integration and Balance. Biomechanical information was monitored at least 3 times. Results: We found significant differences in stride length (p < 0.0001), stride velocity (p < 0.0001), and double support (p < 0.0001) between the offloading footwear group (OG) and the regular footwear group on a group × time interaction. The balance test embracing eyes-open condition revealed a significant difference in Hip Sway (p = 0.004), COM Range ML (p = 0.008), and COM Position (p = 0.004) between the 2 groups. Longitudinally, the offloading group exhibited slight improvement in the performance of gait parameters over time. The stride length (odds ratio 3.54, 95% CI 1.34–9.34, p = 0.018) and velocity (odds ratio 3.13, 95% CI 1.19–8.19, p = 0.033) of OG patients increased, converse to the double-support period (odds ratio 6.20, 95% CI 1.97–19.55, p = 0.002), which decreased. Conclusions: Special wearable devices can accurately monitor gait and balance parameters in patients in real time. The finding reveals the feasibility and effectiveness of advanced wearable sensors in the prevention and management of diabetic foot ulcer and provides a solid background for future research. In addition, the development of foot ulcers in elderly diabetic patients may be associated with changes in gait parameters and the nature of footwear. Even so, larger follow-up studies are needed to validate our findings.

scholarly journals ONE-LEGGED STANCE BALANCE OF OLDER ADULTS WITH AND WITHOUT FALLS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S470-S470
Author(s):  
Edgar R Vieira ◽  
Márcio Oliveira ◽  
Andre Gil ◽  
Karen Fernandes ◽  
Denilson Teixeira ◽  
...  
Keyword(s):  
Older Adults ◽  
Fear Of Falling ◽  
Center Of Pressure ◽  
Area Under The Curve ◽  
Test Duration ◽  
Mobility Impairments ◽  
Eyes Open ◽  
Falls In Older Adults ◽  
Elliptical Area ◽  
Age Range

Abstract Balance impairment is a common problem among older adults. Poor balance in older adults is often associated with mobility impairments, activity limitations and fear of falling in older adults. Thus, balance assessment is useful for early detection of postural control deficits to prevent mobility impairments and falls in older adults. The aim of this study was to assess if balance measures based in center of pressure (COP) parameters during one-legged stance could differentiate between older adults with and without falls in the past 12 months. One-hundred and seventy older adults (50 fallers and 120 non-fallers, age range: 63-72 years) performed three 30s one-legged stance trials with eyes open on a force platform with 30s of rest between each trial. The following variables were evaluated: COP 95% elliptical area, COP velocity in the anterior-posterior and medio-lateral directions, and test duration (how long the participant was able to stay in one-legged stance, up to 30s). Fallers had poorer balance than non-fallers (P ≤0.004). The COP parameters presented an area under the curve between 0.65-0.72, with sensitivity varying from 66 to 78% and specificity from 54 to 68%. There were no significant differences between fallers and non-fallers on test duration (17 vs. 18s, respectively). The findings showed that the fallers had similar duration time, but poorer balance than the non-fallers during one-legged stance. The COP parameters were able to differentiate the balance between fallers and non-fallers with acceptable area under curve, sensitivity and specificity.

scholarly journals Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

2021 ◽  
Vol 10 (9) ◽  
pp. 1804
Author(s):  
Jorge Posada-Ordax ◽  
Julia Cosin-Matamoros ◽  
Marta Elena Losa-Iglesias ◽  
Ricardo Becerro-de-Bengoa-Vallejo ◽  
Laura Esteban-Gonzalo ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
High Speeds ◽  
Controlled Conditions ◽  
Gait Parameters ◽  
Measurement Units ◽  
Accuracy And Repeatability

In recent years, interest in finding alternatives for the evaluation of mobility has increased. Inertial measurement units (IMUs) stand out for their portability, size, and low price. The objective of this study was to examine the accuracy and repeatability of a commercially available IMU under controlled conditions in healthy subjects. A total of 36 subjects, including 17 males and 19 females were analyzed with a Wiva Science IMU in a corridor test while walking for 10 m and in a threadmill at 1.6 km/h, 2.4 km/h, 3.2 km/h, 4 km/h, and 4.8 km/h for one minute. We found no difference when we compared the variables at 4 km/h and 4.8 km/h. However, we found greater differences and errors at 1.6 km/h, 2.4 km/h and 3.2 km/h, and the latter one (1.6 km/h) generated more error. The main conclusion is that the Wiva Science IMU is reliable at high speeds but loses reliability at low speeds.

Center of Pressure Trajectory and Spatiotemporal Gait Parameters When Walking with Limited Knee Flexion

2021 ◽  
Vol 65 (1) ◽  
pp. 1490-1493
Author(s):  
Seobin Choi ◽  
Jieon Lee ◽  
Gwanseob Shin
Keyword(s):  
Knee Flexion ◽  
Stance Phase ◽  
Center Of Pressure ◽  
Dynamic Balance ◽  
Left Knee ◽  
Step Width ◽  
Normal Walking ◽  
Gait Stability ◽  
Gait Parameters ◽  
Stiff Knee

Stiff-knee, which indicates reduced range of knee flexion, may decrease gait stability. Although it is closely related to an increase in fall risk, the effect of limited knee flexion on the balance capacity during walking has not been well studied. This study aimed at examining how walking with limited knee flexion would influence the center of pressure (COP) trajectory and spatiotemporal gait parameters. Sixteen healthy young participants conducted four different walking conditions: normal walking and walking with limited knee flexion of their left knee up to 40 and 20 degrees, respectively. Results show that the participants walked significantly (p<0.05) slower with shorter stride length, wider step width, less cadence, and decreased stance phase when walking with limited knee flexion, compared to normal walking. The increase in the asymmetry and variability of the COP was also observed. It indicates that limited knee flexion during walking might affect the dynamic balance.

scholarly journals Template-Matching-Based Detection of Freezing of Gait Using Wearable Sensors

2018 ◽  
Vol 129 ◽  
pp. 21-27 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Cunda Wang ◽  
Shihong Duan
Keyword(s):  
Template Matching ◽  
Wearable Sensors ◽  
Freezing Of Gait

scholarly journals Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 109
Author(s):  
Binbin Su ◽  
Christian Smith ◽  
Elena Gutierrez Farewik
Keyword(s):  
Wearable Sensors ◽  
Sensor Data ◽  
Measurement Unit ◽  
Deep Convolutional Neural Networks ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Gait Phase ◽  
Robotic Devices ◽  
Phase Recognition ◽  
Five Phases

Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance.

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