Healthcare Application of In-Shoe Motion Sensor for Older Adults: Frailty Assessment Using Foot Motion during Gait.

Frailty poses a threat to the daily lives of healthy older adults, highlighting the urgent need for technologies that can monitor and prevent its progression. Our objective is to demonstrate a method for providing long-term daily frailty monitoring using an in-shoe motion sensor (IMS). We undertook two steps to achieve this goal. Firstly, we used our previously established SPM-LOSO-LASSO (SPM: statistical parametric mapping; LOSO: leave-one-subject-out; LASSO: least absolute shrinkage and selection operator) algorithm to construct a lightweight and interpretable hand grip strength (HGS) estimation model for an IMS. This algorithm automatically identified novel and significant gait predictors from foot motion data and selected optimal features to construct the model. We also tested the robustness and effectiveness of the model by recruiting other groups of subjects. Secondly, we designed an analog frailty risk score that combined the performance of the HGS and gait speed with the aid of the distribution of HGS and gait speed of the older Asian population. We then compared the effectiveness of our designed score with the clinical expert-rated score. We discovered new gait predictors for HGS estimation via IMSs and successfully constructed a model with an "excellent" intraclass correlation coefficient and high precision. Moreover, we tested the model on separately recruited subjects, which confirmed the robustness of our model for other older individuals. The designed frailty risk score also had a large effect size correlation with clinical expert-rated scores. In conclusion, IMS technology shows promise for long-term daily frailty monitoring, which can help prevent or manage frailty for older adults.

Gait classification of knee osteoarthritis patients using shoe-embedded internal measurement units sensor.

BACKGROUND: Knee osteoarthritis negatively affects the gait of patients, especially that of elderly people. However, the assessment of wearable sensors in knee osteoarthritis patients has been under-researched. During clinical assessments, patients may change their gait patterns under the placebo effect, whereas wearable sensors can be used in any environment. METHODS: Sixty patients with knee osteoarthritis and 20 control subjects were included in the study. Wearing shoes with an IMU sensor embedded in the insoles, the participants were required to walk along a walkway. The sensor data were collected during the gait. To discriminate between healthy and knee osteoarthritis patients and to classify different subgroups of knee osteoarthritis patients (patients scheduled for surgery vs. patients not scheduled for surgery; bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis), we used a machine learning approach called the support vector machine. A total of 88 features were extracted and used for classification. FINDINGS: The patients vs. healthy participants were classified with 71% accuracy, 85% sensitivity, and 56% specificity. The "patients scheduled for surgery" vs. "patients not scheduled for surgery" were classified with 83% accuracy, 83% sensitivity, and 81% specificity. The bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis was classified with 81% accuracy, 75% sensitivity, and 79% specificity. INTERPRETATION: Gait analysis using wearable sensors and machine learning can discriminate between healthy and knee osteoarthritis patients and classify different subgroups with reasonable accuracy, sensitivity, and specificity. The proposed approach requires no complex gait factors and is not limited to controlled laboratory settings.

Feature selection, construction and test of model for estimating lower extremity strength of older adults using foot motion measured by an in-shoe motion sensor.

Lower extremity strength (LES) is essential to support activities in daily living. To extend healthy life expectancy of elderly people, early detection of LES weakness is important. In this study, we challenge to develop a method for LES assessment in daily living via an in-shoe motion sensor (IMS). To construct the estimation model, we collected data from 62 subjects. We used the outcome of the five-times-sit-to-stand test to represent the performance of LES as the target variable. Predictors were constructed from the subjects' foot motions measured by the IMS during straight path walking. We used the leave-one-subject-out least absolute shrinkage and selection operator algorithm to select features and construct respective models for the males and females. As a result, the models achieved fair and a good intra-class correlation coefficient agreement between the true and estimation values, with mean absolute errors of 2.14 and 1.21 s (variation of 23.6 and 16.0%), respectively. To validate the models, we separately collected data from 45 subjects. The models successfully predicted 100% and 90% of the male and female subjects' data, respectively, which suggests the robustness of the constructed estimation models. The results suggested that LES can be identified more effectively in daily living by wearing an IMS, and the use of an IMS has the potential for future frailty and fall risk assessment applications.

Estimation of Hand Grip Strength Using Foot motion Measured by In-shoe Motion Sensor.

There is a strong need to assess frailty in daily living. Hand grip strength (HGS) has been proven to be a very important factor for identifying frailty, however it is always assessed under the guidance of facility clinicians. Our purpose is to demonstrate the possibility of providing HGS estimation by using foot-motion signals measured by an in-shoe motion sensor (IMS) embedded in an insole to achieve high precision HGS assessment in daily living. The foot-motion signals were collected from 62 elder participants (27 men and 35 women). Their HGSs were assessed by a hand dynamometer. Gait parameters, individual properties, and predictors derived from foot-motion signal features in one gait cycle were selected as candidates. Statistical parametric mapping analyses were used to generate predictors from the foot-motion signals. Prior to estimation construction, least absolute shrinkage and selection operator was applied to reduce redundant predictors from candidates. Linear regression models for HGS estimation of men and women were constructed. As the results, we discovered new effective predictors for HGS estimation from foot motions and successfully constructed HGS estimation models that achieved "excellent" agreement with the reference according to intra-class coefficients, and mean absolute errors of 2.96 and 2.57 kg for men and women in leave-one-subject-out cross-validation, respectively. These results suggest that HGS can be estimated with high precision by IMS-measured foot motion and more effective frailty identification in daily living is possible through wearing an IMS.

A Basic Study on Detection of Movement State in Stride by Artificial Neural Network for Estimating Stride Length of Hemiplegic Gait Using IMU.

In order to perform rehabilitation training for improving motor function, measurement of movements and evaluation of motor function become effective. In our research group, the method of estimating stride length during walking by using an inertial sensor attached to the foot was developed. However, since the method used thresholds to detect movement state in each stride for calculation of stride length, there was a difficulty in determination of threshold values for each subject and each stride with hemiplegic subjects in some cases. This study aimed at developing an automatic detection method of movement state in stride by artificial neural network (ANN) for hemiplegic gait. In this paper, three-layer ANN and four-layer ANN with feature extraction layers by autoencoder were tested. Teacher signals were obtained from measured sensor signals by the threshold-based method. The ANN with feature extraction layers was shown to be effective for detecting the movement state of healthy subjects and a hemiplegic subject. The movement state detected by ANN was also suggested to be effective in stride length estimation. It is expected to evaluate the ANN-based method using data measured with more hemiplegic subjects.