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.

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.

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.

Human gait has served as a useful barometer of health. Existing studies for automatic categorization of gait have been limited to a binary classification of pathological and non-pathological gait and provided low accuracy in multi-classification. This study aimed to propose a novel approach that can multi-classify gait with no visually discernible difference in characteristics. Sixty-nine participants without gait disturbance were recruited. Twenty-nine of the participants were semi-professional athletes, and 19 were ordinary people. The remaining 21 participants were people with subtle foot deformities. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while walking. The gait spectrograms were acquired by applying time-frequency analyses to the lower body movement signals measured in one stride and used to train the deep convolutional neural network-based classifiers. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The foot, shank, and thigh spectrograms enabled complete classification of the three groups even without requiring a sophisticated process for feature engineering. This is the first study that employed the spectrographic approach in gait classification and achieved reliable multi-classification of gait without observable differences in characteristics using the deep convolutional neural networks.

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being.

Objective: Gait analysis plays a pivotal role in evaluating walking abilities, with recent advancements in digital health stressing the importance of efficient data collection methods. This study aims to classify nine gait types including one normal and eight abnormal gaits, using sequential network-based models and diverse feature combinations obtained from insole sensors. Methods: The dataset was collected using insole sensors from subjects performing 15 m walking with designated gait types. The sensors incorporated pressure sensors and inertial measurement units (IMUs), along with the center of pressure engineered from the pressure readings. A number of deep learning architectures were evaluated for their ability to classify the gait types, focusing on feature sets including temporal parameters, statistical features of pressure signals, center of pressure data, and IMU data. Ablation studies were also conducted to assess the impact of combining features from different modalities. Results: Our results demonstrate that models incorporating IMU features outperform those using different combinations of modalities including individual feature sets, with the top-performing models achieving F1-scores of up to 90% in sample-wise classification and 92% in subject-wise classification. Additionally, an ablation study reveals the importance of considering diverse feature modalities, including temporal parameters, statistical features from pressure signals, center of pressure data, and IMU data, for comprehensive gait classification. Conclusion: Overall, this study successfully developed deep sequential models that effectively classify nine different gait types, with the ablation study underscoring the potential for integrating features from diverse domains to enhance clinical applications, such as intervention for gait-related disorders.