Our aim is to maximize the interpretable information for gait analysis. To achieve this, it is important to find the optimal sensor placement and the parameters that influence the extraction of automatic gait features. We investigated the effect of different anatomical foot locations on Inertial Measurement Unit (IMU) sensor output. We designed and developed an android app to collect real time synchronous sensor output. We selected a set of five anatomical foot locations covering most of the foot regions to place wearable IMU sensors for data collection. Each participant performed a trial in a straight corridor comprising 25 strides of normal walking, a turn-around and another 25 strides. We proposed an automatic gait features extraction method to analyze the data for stride number, distance, speed, length and period of stride, stance, and swing phases during walking. The highest accuracy for detecting stride number was in location 1 (first cuneiform) followed by location 5 (Achilles Tendon) and 4 (Talus). Location 1 was the closest to correlate estimate to the measured distance travelled. The accuracy of detecting number of strides on average is 95.47% from accelerometer data and 93.60% from gyroscope data and closest to the 60:40% split for average stance and swing for 15 subjects. To validate our results, we conducted trials using the Qualisys motion capture instrument and from our sensors concurrently. The average accuracy of the result is 97.77% with 95% confidence interval 0.767 for Estimated and 99.01% with 95% confidence interval 0.266 for Period.

This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment.

Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict falls before they happen, provide safeguards during the fall, and issue remote notifications after the fall is essential to improving the level of care for the elderly. This study proposed a concept for a wearable monitoring framework that aims to anticipate falls during their beginning and descent, activating a safety mechanism to minimize fall-related injuries and issuing a remote notification after the body impacts the ground. However, the demonstration of this concept in the study involved the offline analysis of an ensemble deep neural network architecture based on a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) and existing data. It is important to note that this study did not involve the implementation of hardware or other elements beyond the developed algorithm. The proposed approach utilized CNN for robust feature extraction from accelerometer and gyroscope data and RNN to model the temporal dynamics of the falling process. A distinct class-based ensemble architecture was developed, where each ensemble model identified a specific class. The proposed approach was evaluated on the annotated SisFall dataset and achieved a mean accuracy of 95%, 96%, and 98% for Non-Fall, Pre-Fall, and Fall detection events, respectively, outperforming state-of-the-art fall detection methods. The overall evaluation demonstrated the effectiveness of the developed deep learning architecture. This wearable monitoring system will prevent injuries and improve the quality of life of elderly individuals.

Conditions causing gait abnormalities are very common and their treatment requires the detailed assessment of gait. Currently such assessments are carried out in gait laboratories and require the use of complex and expensive equipment. To increase availability and use at home and clinics, we design and develop an affordable, user friendly, wireless, portable automatic system to extract spatiotemporal features of gait that can be used indoors and outdoors. This study determines the concurrent validity of extracted gait features from Inertial Measurement Units (IMUs) against `gold standard' Motion Capture System (MoCap) using a hybrid gait features extraction method. The analysis of the proposed method is based on minimum prominence and abrupt transition points in the IMU signals. We also compare the degree of agreement for mean spatiotemporal gait features. The concurrent data from synchronized IMUs and MoCap are collected from 18 subjects. We validate our proposed system using two experiments; 1) IMU and MoCap with self-selected (free) walking and 2) IMU and MoCap at various walking speeds. Interclass correlations, Lin's concordance correlation coefficients and Pearson's correlation coefficients (r) are applied to determine the correlation between extracted gait features from IMU and MoCap measurements. Bland-Altman plots are also generated to evaluate any unknown bias between the mean extracted features. The experiments show that spatiotemporal features of gait extracted from IMUs are highly valid. Our methods facilitate gait assessment in clinics and at home including the possibility of self-assessment.

Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two Inertial Measurement Unit (IMU) sensors through Bluetooth connectivity. The data is collected from 12 young (10 for modelling and 2 for validation) and 20 older subjects. We analyse the data collected from real world for stride, step, stance and swing gait features. We validate our method with measurements of gait features. Generalized Procrustes analysis is used to estimate a standard normal mean gait shape (NMGS) for 10 young subjects. Each gait feature of both young and older subjects is then converted to find the best match with the NMGS using ordinary Procrustes analysis. The shape distance between the NMGS and each gait shape is estimated using Riemannian shape distance, Riemannian size-and-shape distance, Procrustes size-and-shape distance and Root mean square deviation. A t-test is performed to provide statistical evidence of gait shape differences between young and older gaits. A mean form which is considered as a standard normal mean gait form (NMGF) and inter-feature distances are estimated from the set of 10 young subjects. The form difference is estimated between the NMGF and individual gaits of young and older. The degree of abnormality is then estimated for individual features and the result is plotted to visualize the feature in a gait. Experimental results demonstrate the performance of the proposed method.