Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to improve the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data were gathered from elderly subjects equipped with three IMU sensors while they walked. The input taken only from the waist sensor was used to train 16 deep-learning models including a CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. A fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of ±6 TS (±6 ms) and ±1 TS (±1 ms), respectively. Advancing from the previous studies exploring gait event detection, the model demonstrated a great improvement in terms of its prediction error having an MAE of 6.239 ms and 5.24 ms for HS and TO events, respectively, at the tolerance window of ±1 TS. The results demonstrated that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon.

An accurate and credible measurement of human gait is essential in multiple areas of medical science and rehabilitation. Yet, the methods currently available are not only arduous but also costly. Researchers who investigated the relationship between foot and gait parameters have found that the two parameters are closely interrelated and suggested that measuring foot characteristics can be an alternative to the strenuous quantification currently in use. This study aims to verify the potential of foot characteristics in predicting the actual gait temporo-spatial parameters and to develop a deep neural network (DNN) model that can estimate and quantify the gait temporo-spatial parameters from foot characteristics. The foot features in sitting, standing, and one-leg standing conditions of 42 subjects were used as the input data and gait temporo-spatial parameters at fast, normal, and slow speed were set as the output of the DNN regressor. With the prediction accuracy of 95% or higher, the feasibility of the developed model was verified. This study might be the first in attempting experimental verification of the foot features serving as predictors of individual gait. The DNN regressor will help researchers improve the data pool with less labor and expense when some limitations get properly overcome.

Faced with the rapidly aging world population, frailty has emerged as a major health burden among the elderly. This study aimed to investigate the feasibility of using temporal gait characteristics and a long short-term memory network for assessing frailty. Seventy-four community-dwelling elderly individuals participated in this study. The participants were categorized into three groups by their FRAIL scale: robust, pre-frail, and frail groups. The participants completed a 7-meter walking at the self-selected pace with a gyroscope on each foot. Analyzing the gyroscopic data produced seven temporal gait parameters per each gait cycle. Enumerating six consecutive values of each gait parameter produced the gait sequence features which were used as frailty predictors along with the demographic features. Five-fold cross-validation was applied to 70% of the data, and the remaining 30% were used as test data. An F ₁ -score of 0.931 was achieved in classifying the robust, pre-frail, and frail groups by the random forest model trained with age, sex, and the outputs of the long short-term memory network-based classifier that used the initial and terminal double-limb support, step, and stride times as inputs. The proposed approach of assessing frailty using the arrhythmic gait pattern of the elderly and machine learning technique is novel and promising. Pioneering a way that self-monitor frailty at home without any help from experts, the study can contribute toearly diagnosis of frailty and make timely medical intervention possible.

Recent advanced interface technologies allow the user to interact with different spaces such as virtual reality (VR), augmented reality (AR) and ubiquitous computing (UC) spaces. Previously, human computer interaction (HCI) issues in VR, AR and UC have been largely carried out in separate communities. Here, we combine these three interaction spaces into a single interaction space, called tangible space. We propose the VARU framework which is designed for rapid prototyping of a tangible space application. It is designed to provide extensibility, flexibility and scalability. Depending on the available resources, the user could interact with either the virtual, physical or mixed environment. By having the VR, AR and UC spaces in a single platform, it gives us the possibility to explore different types of collaboration across the different spaces. As a result, we present our prototype application which is built using the VARU framework.

Cognitive impairment in the elderly causes a significant decline in the quality of life and a substantial economic burden on society. Yet, diagnosing cognitive impairment is apt to be missed or delayed due to its assessment being laborious. This study aimed to propose a new approach of classifying the risk of cognitive impairment in the elderly using sequential gait characteristics and machine learning techniques. A total of 108 community-dwelling elderly individuals participated in this study. The participants were categorized into three groups based on their scores of the mini-mental state examination. Each participant completed both the usual- and fast-paced walking on the straight path with two gyroscopes on each foot. By analyzing the foot sagittal angular velocity signals, the temporal gait parameters were extracted from each gait cycle. Five classical machine learning models and a long short-term memory network were respectively employed to produce the classifiers that used the time-consecutive temporal gait parameters as predictors of cognitive impairment. Five-fold cross-validation was applied to 70% of the data in each group, and the remaining 30% were used as test data. An F ₁ -score of 0.974 was achieved in classifying the three groups by the long short-term memory network-based classifier that used the double-limb support, stance, step, and stride times at usual-paced walking and the double- and single-limb support, stance, and stride times at fast-paced walking as inputs. The proposed approach would pave the way for earlier diagnosis of cognitive impairment in non-clinical settings without professional help, which can facilitate more timely intervention.

The conventional method of measuring foot-arch parameters is highly dependent on the measurer’s skill level, so accurate measurements are difficult to obtain. To solve this problem, we propose an autonomous geometric foot-arch analysis platform that is capable of capturing the sole of the foot and yields three foot-arch parameters: arch index (AI), arch width (AW) and arch height (AH). The proposed system captures 3D geometric and color data on the plantar surface of the foot in a static standing pose using a commercial RGB-D camera. It detects the region of the foot surface in contact with the footplate by applying the clustering and Markov random field (MRF)-based image segmentation methods. The system computes the foot-arch parameters by analyzing the 2/3D shape of the contact region. Validation experiments were carried out to assess the accuracy and repeatability of the system. The average errors for AI, AW, and AH estimation on 99 data collected from 11 subjects during 3 days were −0.17%, 0.95 mm, and 0.52 mm, respectively. Reliability and statistical analysis on the estimated foot-arch parameters, the robustness to the change of weights used in the MRF, the processing time were also performed to show the feasibility of the system.