Abstract:Instrumented footwear represents a promising technology for spatiotemporal gait analysis in out-of-the-lab conditions. However, moderate accuracy impacts this technology's ability to capture subtle, but clinically meaningful, changes in gait patterns that may indicate adverse outcomes or underlying neurological conditions. This limitation hampers the use of instrumented footwear to aid functional assessments and clinical decision making. This paper introduces new transductivelearning inference models that subs… Show more
“…We therefore expected to find more differences between walking with at rollator on bitumen than we did, which might depend on our sample of healthy, younger persons. Using wearable sensors to measure gait with walking aid can reduce the accuracy of the measure, which also could explain the small differences [ 25 ]. However, step length is related to fear of falling among older adults [ 26 ] and fear of falling and falls are also related [ 27 ].…”
Section: Discussionmentioning
confidence: 99%
“…Using additional IMU:s is for example reliable for measuring trunk range of motion [ 34 ] and to detect anticipatory postural adjustments [ 35 ]. Instrumental footwear has also shown to be feasible for ambulatory gait analysis [ 25 ]. However, we used the same lap in all walks, which increases reliability of the data collected.…”
Objectives
Gait parameters can measure risks of falling and mortality and identify early stages of frailty. The use of walking aid changes gait parameters. The aim of this study was to describe differences in gait parameters among healthy adults when walking on different surfaces and under different conditions, with and without a rollator.
Results
Ten healthy participants walked first without and then with a rollator upslope, downslope and on flat surface, on bitumen and gravel respectively. Step length, walking speed and sideway deviation was measured using an inertial measurement unit. Walking up a slope using a rollator generated the longest step length and walking down a slope using a rollator the shortest. Fastest walking speed was used when walking up a slope with rollator and slowest when walking down a slope with rollator. Sideway deviation was highest when walking down a slope and lowest when walking on gravel, both without rollator. Highest walk ratio was found when walk up a slope without rollator and lowest when walking down a slope with rollator. Data from this study provides valuable knowledge regarding gait parameters among healthy individuals, useful for future clinical research relevant for rehabilitation and public health.
“…We therefore expected to find more differences between walking with at rollator on bitumen than we did, which might depend on our sample of healthy, younger persons. Using wearable sensors to measure gait with walking aid can reduce the accuracy of the measure, which also could explain the small differences [ 25 ]. However, step length is related to fear of falling among older adults [ 26 ] and fear of falling and falls are also related [ 27 ].…”
Section: Discussionmentioning
confidence: 99%
“…Using additional IMU:s is for example reliable for measuring trunk range of motion [ 34 ] and to detect anticipatory postural adjustments [ 35 ]. Instrumental footwear has also shown to be feasible for ambulatory gait analysis [ 25 ]. However, we used the same lap in all walks, which increases reliability of the data collected.…”
Objectives
Gait parameters can measure risks of falling and mortality and identify early stages of frailty. The use of walking aid changes gait parameters. The aim of this study was to describe differences in gait parameters among healthy adults when walking on different surfaces and under different conditions, with and without a rollator.
Results
Ten healthy participants walked first without and then with a rollator upslope, downslope and on flat surface, on bitumen and gravel respectively. Step length, walking speed and sideway deviation was measured using an inertial measurement unit. Walking up a slope using a rollator generated the longest step length and walking down a slope using a rollator the shortest. Fastest walking speed was used when walking up a slope with rollator and slowest when walking down a slope with rollator. Sideway deviation was highest when walking down a slope and lowest when walking on gravel, both without rollator. Highest walk ratio was found when walk up a slope without rollator and lowest when walking down a slope with rollator. Data from this study provides valuable knowledge regarding gait parameters among healthy individuals, useful for future clinical research relevant for rehabilitation and public health.
“…The hardware and software require approximately 5 minutes to equip onto patients and initiate data collection. This technology has been validated against gold-standard gait analysis systems in young healthy individuals (Zhang et al, 2020), older adults (Zhang et al, 2022), and patients with neuromuscular (Duong et al, 2021) or neurodevelopmental (Duong et al, 2020) disorders. Figure 1. Components of the instrumented insole system, including (a) an 8-cell force sensitive resistor (FSR) and 9-degree-of-freedom inertial measurement unit (IMU), (b) a smartphone control application, and (c) the fully assembled insole and logic unit.…”
Background
Imbalance and gait disturbances are common in patients with vestibular schwannoma (VS) and can result in significant morbidity. Current methods for quantitative gait analysis are cumbersome and difficult to implement. Here, we use custom-engineered instrumented insoles to evaluate the gait of patients diagnosed with VS.
Methods
Twenty patients with VS were recruited from otology, neurosurgery, and radiation oncology clinics at a tertiary referral center. Functional gait assessment (FGA), 2-minute walk test (2MWT), and uneven surface walk test (USWT) were performed. Custom-engineered instrumented insoles, equipped with an 8-cell force sensitive resistor (FSR) and a 9-degree-of-freedom inertial measurement unit (IMU), were used to collect stride-by-stride spatiotemporal gait parameters, from which mean values and coefficients of variation (CV) were determined for each patient.
Results
FGA scores were significantly correlated with gait metrics obtained from the 2MWT and USWT, including stride length, stride velocity, normalized stride length, normalized stride velocity, stride length CV, and stride velocity CV. Tumor diameter was negatively associated with stride time and swing time on the 2MWT; no such association existed between tumor diameter and FGA or DHI.
Conclusions
Instrumented insoles may unveil associations between VS tumor size and gait dysfunction that cannot be captured by standardized clinical assessments and self-reported questionnaires.
“…To reduce the energy costs of exoskeletons during loaded walking, some motion intent recognition schemes [6,7] detect the key timing of the GC. To analyze the gait features of the elderly, Zhang et al [8] detected the subject's gait phase, such as stride length and swing time. For the intra-and inter-subject clinical diagnoses, Morbidoni et al [9] predicted heel-strike and toe-off timing of cerebral-palsy children.…”
Objective.
The gait phase and joint angle are two essential and complementary components of kinematics during normal walking, whose accurate prediction is critical for lower-limb rehabilitation, such as controlling the exoskeleton robots. Multi-modal signals have been used to promote the prediction performance of the gait phase or joint angle separately, but it is still few reports to examine how these signals can be used to predict both simultaneously. 
Approach.
To address this problem, we propose a new method named transferable multi-modal fusion (TMMF) to perform a continuous prediction of knee angles and corresponding gait phases by fusing multi-modal signals. Specifically, TMMF consists of a multi-modal signal fusion block, a time series feature extractor, a regressor, and a classifier. The multi-modal signal fusion block leverages the Maximum Mean Discrepancy to reduce the distribution discrepancy across different modals in the latent space, achieving the goal of transferable multi-modal fusion. Subsequently, by using the long short-term memory-based network, we obtain the feature representation from time series data to predict the knee angles and gait phases simultaneously. To validate our proposal, we design an experimental paradigm with random walking and resting to collect data containing multi-modal biomedical signals from electromyography, gyroscopes, and virtual reality.
Main results.
Comprehensive experiments on our constructed dataset demonstrate the effectiveness of the proposed method. TMMF achieves a root mean square error of 0.090±0.022 s in knee angle prediction and a precision of 83.7±7.7\% in gait phase prediction.
Significance.
We demonstrate the feasibility and validity of using TMMF to predict lower-limb kinematics continuously from multi-modal biomedical signals. This proposed method represents application potential in predicting the motor intent of patients with different pathologies.
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