Validation of an Ear-Worn Wearable Gait Analysis Device

Assessment of Gait Parameters Using Wearable Sensors and Their Association With Muscle Mass, Strength, and Physical Performance in Korean Older Adults: Cross-Sectional Study

Background

Gait speed indicates the onset or decline of physical performance in sarcopenia. However, real-time measurements of other gait parameters, such as step length, stride length, step width, and support time, are limited. The advent of wearable technology has facilitated the measurement of these parameters, necessitating further investigation into their potential applications.

Objective

This study aimed to investigate the relationship between gait parameters measured using wearable sensors and muscle mass, strength, and physical performance in community-dwelling older adults.

Methods

In a cross-sectional study of 91 participants aged ≥65 years, gait parameters, such as step count, step length, cadence, single and double support times, vertical oscillation, and instantaneous vertical loading rate (IVLR), measured using a wireless earbud device, were analyzed on the basis of the appendicular skeletal muscle mass index (SMI), calf circumference, handgrip strength, 5-time chair stand test, short physical performance battery (SPPB), and the SARC-F (strength, assistance with walking, rise from a chair, climb stairs and fall frequency) questionnaire. This study was conducted from July 10 to November 1, 2023, at an outpatient clinic of a university hospital in Seoul, Korea. Multiple regression analysis was performed to investigate independent associations after adjusting for age, sex, BMI, and comorbidities.

Results

Among 91 participants (45 men and 46 women; mean age 74.1 years for men and 73.6 years for women), gait speed and vertical oscillation showed negative associations with their performance in the 5-time chair stand test (P<.001) and SARC-F and positive associations with their performance in the SPPB (P<.001). Vertical oscillations were also associated with grip strength (P=.003). Single and double support times were associated with performance in the 5-time chair stand test and SPPB (P<.001). In addition, double support time was associated with SARC-F scores (P<.001). Gait speed, support time, vertical oscillation, and IVLR showed independent associations with performance in the 5-time chair stand test and SPPB (P<.001), both related to muscle strength or physical performance. Gait speed, double support time, and vertical oscillation all had significant associations with SARC-F scores.

Conclusions

This study demonstrated a significant association between gait monitoring using wearable sensors and quantitative assessments of muscle strength and physical performance in older people. Furthermore, this study substantiated the extensive applicability of diverse gait parameters in predicting sarcopenia.

Estimating Gait Speed in the Real World With a Head-Worn Inertial Sensor

Head-worn inertial sensors represent a valuable option to characterize gait in real-world conditions, thanks to the integration with glasses and hearing aids. Few methods based on head-worn sensors allow for stride-by-stride gait speed estimation, but none has been developed with data collected in real-world settings. This study aimed at validating a two-steps machine learning method to estimate initial contacts and stride-by-stride speed in real-world gait using a single inertial sensor attached to the temporal region. A convolutional network is used to detect strides. Then, stride-by-stride gait speed is inferred from the detected cycles by a gaussian process regression model. A multi-sensor wearable system was used to label over 100,000 strides recorded from 15 healthy young adults during supervised acquisitions and real-world unsupervised walking. The stride detector achieved high detection rate (F1-score > 92%) and accuracy (mean absolute error < 40 ms). Very strong correlation between target and predicted speed (Spearman coefficient > 0.86) and low mean absolute error (< 0.085 m/s) were observed. The method proved valid for the quantitative evaluation of stride-by-stride gait speed in real-world conditions. These findings lay the technical and analytical groundwork for future clinical validation and application of gait analysis frameworks that integrate inertial sensors with head-worn devices.

Step Length and Gait Speed Estimation Using a Hearing Aid Integrated Accelerometer: A Comparison of Different Algorithms

Gait is an indicator of a person's health status and abnormal gait patterns are associated with a higher risk of falls, dementia, and mental health disorders. Wearable sensors facilitate long-term assessment of walking in the user's home environment. Earables, wearable sensors that are worn at the ear, are gaining popularity for digital health assessments because they are unobtrusive and can easily be integrated into the user's daily routine, for example, in hearing aids. A comprehensive gait analysis pipeline for an ear-worn accelerometer that includes spatial-temporal parameters is currently not existing. Therefore, we propose and compare three algorithmic approaches to estimate step length and gait speed based on ear-worn accelerometer data: a biomechanical model, feature-based machine learning (ML) models, and a convolutional neural network. We evaluated their performance on a step and walking bout level and compared it with an optical motion capture system. The feature-based ML model achieved the best performance with a precision of 4.8cm on a walking bout level. For gait speed, the machine learning approach achieved an absolute percentage error of 5.4 % ( ± 4.0 %). We find that the ML model is able to estimate step length and gait speed with clinically relevant precision. Furthermore, the model is insensitive to different age groups and sampling rates but sensitive to walking speed. To our knowledge, this work is the first contribution to estimating step length and gait speed using ear-worn accelerometers. Moreover, it lays the foundation for a comprehensive gait analysis framework for ear-worn sensors enabling continuous and long-term monitoring at home.

The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.

Innovation in Digital Health Interventions for Frailty and Sarcopenia

Today, the challenges of an aging society are primarily seen in frailty, sarcopenia, and impaired functionality [...]