Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases

Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors' Locations

Portable monitoring devices based on Inertial Measurement Units (IMUs) have the potential to serve as quantitative assessments of human movement. This article proposes a new method to identify the optimal placements of the IMUs and quantify the smoothness of the gait. First, it identifies gait events: foot-strike (FS) and foot-off (FO). Second, it segments the signals of linear acceleration and angular velocities obtained from the IMUs at four locations into steps and strides. Finally, it applies three smoothness metrics (SPARC, PM, and LDLJ) to determine the most reliable metric and the best location for the sensor, using data from 20 healthy subjects who walked an average of 25 steps on a flat surface for this study (117 measurements were processed). All events were identified with less than a 2% difference from those obtained with the photogrammetry system. The smoothness metric with the least variance in all measurements was SPARC. For the smoothness metrics with the least variance, we found significant differences between applying the metrics with the complete signal (C) and the signal segmented by strides (S). This method is practical, time-effective, and low-cost in terms of computation. Furthermore, it is shown that analyzing gait signals segmented by strides provides more information about gait progression.

Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices' performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement.

Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review

In recent years, marked progress has been made in wearable technology for human motion and posture recognition in the areas of assisted training, medical health, VR/AR, etc. This paper systematically reviews the status quo of wearable sensing systems for human motion capture and posture recognition from three aspects, which are monitoring indicators, sensors, and system design. In particular, it summarizes the monitoring indicators closely related to human posture changes, such as trunk, joints, and limbs, and analyzes in detail the types, numbers, locations, installation methods, and advantages and disadvantages of sensors in different monitoring systems. Finally, it is concluded that future research in this area will emphasize monitoring accuracy, data security, wearing comfort, and durability. This review provides a reference for the future development of wearable sensing systems for human motion capture.

The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing

Transfer learning is a growing field that can address the variability of activity recognition problems by reusing the knowledge from previous experiences to recognise activities from different conditions, resulting in the leveraging of resources such as training and labelling efforts. Although integrating ubiquitous sensing technology and transfer learning seem promising, there are some research opportunities that, if addressed, could accelerate the development of activity recognition. This paper presents TL-FmRADLs; a framework that converges the feature fusion strategy with a teacher/learner approach over the active learning technique to automatise the self-training process of the learner models. Evaluation TL-FmRADLs is conducted over InSync; an open access dataset introduced for the first time in this paper. Results show promising effects towards mitigating the insufficiency of labelled data available by enabling the learner model to outperform the teacher’s performance.

Towards automated assessment of frailty status using a wrist-worn device

Wearable sensors potentially enable monitoring the users physical activity in daily life. Therefore, they are particularly appealing for the evaluation of older subjects in their environment, to capture early signs of frailty and mobility-related problems. This study explores the use of body-worn accelerometers for automated assessment of frailty during walking activity. Experiments involved 34 volunteers aged 70+, who were initially screened by geriatricians for the presence of frailty according to Frieds criteria. After screening, the volunteers were asked to walk 60 m at preferred speed, while wearing two accelerometers, one positioned on the lower back and the other on the wrist. Sensor-derived signals were analyzed independently to compare the ability of the two signals (wrist vs. lower back) in frailty status assessment. A gait detection technique was applied to identify segments made of four gait cycles. These segments were then used as input to compute 25 features in time and time-frequency domains, the latter by means of the Wavelet Transform. Finally, five machine learning models were trained and evaluated to classify subjects as robust or non-robust (i.e., pre-frail or frail). Gaussian naive Bayes applied to the features derived from the wrist sensor signal identified non-robust subjects with 91% sensitivity and 82% specificity, compared to 87% sensitivity and 64% specificity achieved with the lower back sensor. Results demonstrate that a wrist-worn accelerometer provides valuable information for the recognition of frailty in older adults, and could represent an effective tool to enable automated and unobtrusive assessment of frailty.

A novel gait parameter estimation method for healthy adults and postoperative patients with an ear-worn sensor

OBJECTIVE

Gait analysis helps to assess recovery during rehabilitation. Previous gait analysis studies are primarily applicable to healthy subjects or to postoperative patients. The purpose of this paper is to construct a new gait parameter estimation platform based on an ear-worn activity recognition (e-AR) sensor, which can be used for both normal and pathological gait signals.

APPROACH

Thirty healthy adults and eight postoperative patients participated in the experiment. A method based on singular spectrum analysis (SSA) and iterative mean filtering (IMF) is proposed to detect gait events and estimate three key gait parameters, i.e. stride time, swing time, and stance time.

MAIN RESULTS

Experimental results show that the estimated gait parameters provided by the proposed method are very close to the gait parameters provided by the gait assessment system. For normal gait signals, the average absolute errors of stride, swing, and stance time are 27.8 ms, 35.8 ms, and 37.5 ms, respectively. For pathological gait signals, the average absolute error of stride time is 32.1 ms.

SIGNIFICANCE

The proposed parameter estimation method can be applied to both general analysis for healthy subjects and rehabilitation evaluation for postoperative patients. The convenience and comfort of the ear-worn sensor increase its potential for practical applications.

Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds

The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects’ step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm.

Soft-Material-Based Smart Insoles for a Gait Monitoring System

Spatiotemporal analysis of gait pattern is meaningful in diagnosing and prognosing foot and lower extremity musculoskeletal pathologies. Wearable smart sensors enable continuous real-time monitoring of gait, during daily life, without visiting clinics and the use of costly equipment. The purpose of this study was to develop a light-weight, durable, wireless, soft-material-based smart insole (SMSI) and examine its range of feasibility for real-time gait pattern analysis. A total of fifteen healthy adults (male: 10, female: 5, age 25.1 ± 2.64) were recruited for this study. Performance evaluation of the developed insole sensor was first executed by comparing the signal accuracy level between the SMSI and an F-scan. Gait data were simultaneously collected by two sensors for 3 min, on a treadmill, at a fixed speed. Each participant walked for four times, randomly, at the speed of 1.5 km/h (C1), 2.5 km/h (C2), 3.5 km/h (C3), and 4.5 km/h (C4). Step count from the two sensors resulted in 100% correlation in all four gait speed conditions (C1: 89 ± 7.4, C2: 113 ± 6.24, C3: 141 ± 9.74, and C4: 163 ± 7.38 steps). Stride-time was concurrently determined and R2 values showed a high correlation between the two sensors, in both feet (R² ≥ 0.90, p < 0.05). Bilateral gait coordination analysis using phase coordination index (PCI) was performed to test clinical feasibility. PCI values of the SMSI resulted in 1.75 ± 0.80% (C1), 1.72 ± 0.81% (C2), 1.72 ± 0.79% (C3), and 1.73 ± 0.80% (C4), and those of the F-scan resulted in 1.66 ± 0.66%, 1.70 ± 0.66%, 1.67 ± 0.62%, and 1.70 ± 0.62%, respectively, showing the presence of a high correlation (R² ≥ 0.94, p < 0.05). The insole developed in this study was found to have an equivalent performance to commercial sensors, and thus, can be used not only for future sensor-based monitoring device development studies but also in clinical setting for patient gait evaluations.