Estimating Indicators for Assessing Knee Motion Impairment During Gait Using In-Shoe Motion Sensors: A Feasibility Study.

Knee joint function deterioration significantly impacts quality of life. This study developed estimation models for ten knee indicators using data from in-shoe motion sensors to assess knee movement during everyday activities. Sixty-six healthy young participants were involved, and multivariate linear regression was employed to construct the models.

Differences in kinematic parameters during gait between the patients with knee osteoarthritis and healthy controls using an insole with a single inertial measurement unit: A case-control study.

Background: An inertial measurement unit is small and lightweight, allowing patient measurements without physical constraints. This study aimed to determine the differences in kinematic parameters during gait using an insole with a single inertial measurement unit in healthy controls and on both sides in patients with knee osteoarthritis.

Methods: Twenty patients with knee osteoarthritis and 13 age-matched controls were included in this study.

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).

Gait characteristics in patients with distal radius fracture using an in-shoe inertial measurement system at various gait speeds.

Background: Distal radius fractures (DRF) commonly occur in early postmenopausal females as the first fragility fracture. Although the incidence of DRF in this set of patients may be related to a lower ability to control their balance and gait, the detailed gait characteristics of DRF patients have not been examined.

Research Question: Is it possible to identify the physical and gait features of DRF patients using in-shoe inertial measurement unit (IMU) sensors at various gait speeds and to develop a machine learning (ML) algorithm to estimate patients with DRF using gait?

Methods: In this cross-sectional case control study, we recruited 28 postmenopausal females with DRF as their first fragility fracture and 32 age-matched females without a history of fragility fractures.

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.

Identification of characteristics of foot position and angle during swing phase in fallers using principal component analysis.

Identifying the characteristics of fallers is important for preventing falls because such events may reduce quality of life. It has been reported that several variables related to foot positions and angles during gait (e.g.

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.

Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach.

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC).

An algorithm for real time minimum toe clearance estimation from signal of in-shoe motion sensor.

An algorithm has been constructed for estimating minimum toe clearance (MTC), an important gait parameter previously proven to be a critical indicator of tripping risk. It uses data from a previously reported in-shoe motion sensor (IMS) for detecting gait events. First, candidate feature points in the IMS signal for use in detecting MTC events were identified.