Validity of Mobility Lab (version 2) for gait assessment in young adults, older adults and Parkinson’s disease

Abstract

Objective: Gait provides a sensitive measurement for signs of aging and neurodegenerative conditions. Measurement of gait is transitioning from the laboratory environment to the clinic with the use of inertial measurement units, providing a simple and cost-effective assessment tool. However, such assessments first need validation against reference systems. The aim of this study was to validate the APDM Mobility Lab (ML) system (version 2) against a pressure sensor walkway in younger adults (n = 18), older adults (n = 18) and people with mild Parkinson's disease (n = 21) in the laboratory. Approach: Participants completed a two-minute walk over a pressure sensor walkway whilst wearing three sensors (strapped to the lumbar spine and both feet). Comparison of output from the systems was then performed. Main results: Overall, we identified that ML provided good to excellent agreement (ICC > 0.75) for gait velocity, stride length, stride length SD, cadence, stride time and stride time SD. Measures of double support time, single support time and swing time had moderate to poor agreement (ICC 0.213-0.725), particularly for younger adults and PD. Significance: Overall, Mobility Lab provides a valid system for gait data collection for clinical and research application.

Kinematics and temporospatial parameters during gait from inertial motion capture in adults with and without HIV: a validity and reliability study

“…Table 3 Absolute reliability (measurement error) of gait parameters measured by inertial measurement units SEM 95% CL were calculated using a sample-and-trial-specific multiplying factor of 1.2 [19] %SEM absolute percentage SEM, CL confidence limits of SEM, GC gait cycle, PLHIV people living with HIV-1 infection, SEM standard error of measurement, SNP HIV-seronegative participants Double support time and those parameters expressed as a percentage of the gait cycle (especially double support percentage) showed the largest relative differences and/or worst ICCs in both participant groups. Similar results have been reported recently in a study validating a three-IMU system relative to an instrumented walkway [25]. Errors for double support time and percentage, and single support percentage, tended to be lower in PLHIV relative to SNP.…”

“…These differences may stem from gait speed differences and/or true pathology [25], although further research is needed to support such speculations. Other IMU validation studies comparing (slower walking) older adults to (faster Table 4 Absolute reliability (measurement error) of kinematic angles measured by inertial measurement units A1 corresponding to A1 power phase of ankle, A2 corresponding to A2 power phase of ankle, H3 corresponding to H3 power phase of hip, HR heel rise, IC initial contact, K1 corresponding to K1 power phase of knee, K2 corresponding to K2 power phase of knee, K3 corresponding to K3 power phase of knee, LR loading response, MSt mid-stance, PLHIV people living with HIV-1 infection, ROM range of motion, SEM standard error of measurement, SNP HIV-seronegative participants, TO toe-off, TSt terminal stance walking) younger adults have had similar findings for these parameters (lower errors for these outcomes in the older adults) [25,26]. Differences between the IMU and OMC systems were more apparent when comparing (discrete) kinematic angles at specific time points of the gait cycle, and less so when comparing relative joint/segment ROM.…”

Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

“…In addition, we manually selected those 48 features based on the clinical expertise and prior-knowledge [45]. A larger sample size will allow us to include all 130 features and employ different feature selection methods in machine learning such as filter- Table 2 Model hyper-parameters of the classification models Note: Adam was used for learning rate optimization of NN [43]; gamma hyper-parameter in SVM was applied when the kernel is radial basis function 'rbf'; class_weight was applied when the oversampling approach (SMOTE) was not used. Further details about hyper-parameters used in this study can be found: NN (https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html), SVM (https://scikit-learn.org/stable/modules/generated/sklearn.svm.…”

Towards ecologically valid biomarkers: real-life gait assessment in cerebellar ataxia

“…decreased walking speed), but features inherent to ataxic gait changes in DCA-as it will be these features that will be particularly sensitive to change by upcoming treatment trials specifically targeting cerebellar Out of the respective total groups, 21 CA patients and 17 HC were available for the RLW condition (for an overview of these subjects, see Table 1). Step events, as well as spatio-temporal gait parameters from the IMU sensors were extracted using APDM's mobility lab software (Version 2) 31 , which has been shown to deliver good-to-excellent accuracy and repeatability 32,33 . For each detected stride, the following features were extracted: stride length, stride time, lateral step deviation and raw accelerometer data of the lumbar sensor.…”