Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors

Recognition of movement is a crucial process to assist humans in activities of daily living, such as walking. In this work, a high-level method for the simultaneous recognition of locomotion and gait phases using wearable sensors is presented. A Bayesian formulation is employed to iteratively accumulate evidence to reduce uncertainty, and to improve the recognition accuracy. This process uses a sequential analysis method to autonomously make decisions, whenever the recognition system perceives that there is enough evidence accumulated. We use data from three wearable sensors, attached to the thigh, shank, and foot of healthy humans. Level-ground walking, ramp ascent and descent activities are used for data collection and recognition. In addition, an approach for segmentation of the gait cycle for recognition of stance and swing phases is presented. Validation results show that the simultaneous Bayesian recognition method is capable to recognize walking activities and gait phases with mean accuracies of 99.87% and 99.20%. This process requires a mean of 25 and 13 sensor samples to make a decision for locomotion mode and gait phases, respectively. The recognition process is analyzed using different levels of confidence to show that our method is highly accurate, fast, and adaptable to specific requirements of accuracy and speed. Overall, the simultaneous Bayesian recognition method demonstrates its benefits for recognition using wearable sensors, which can be employed to provide reliable assistance to humans in their walking activities

Probabilistic locomotion mode recognition with wearable sensors

Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to assist humans in walking activities. We present a probabilistic Bayesian approach with a sequential analysis method for recognition of locomotion and phases of the gait cycle. Our approach uses recursive accumulation of evidence, as biological systems do, to reduce uncertainty present in the sensor measurements, and thus improving recognition accuracy. Data were collected from a wearable sensor, attached to the shank of healthy human participants, from three locomotion modes; level-ground walking, ramp ascent and ramp descent. We validated our probabilistic approach with recognition of locomotion in steady-state and gait phases in transitional states. Furthermore, we evaluated the effect, in recognition accuracy, of the accumulation of evidence controlled by increasing belief thresholds. High accuracy results achieved by our approach, demonstrate its potential for robust control of lower limb wearable soft robotic devices to provide natural and safe walking assistance to humans

Prediction of gait events in walking activities with a Bayesian perception system

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots

Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors

In this paper, a novel approach for recognition of walking activities and gait events with wearable sensors is presented. This approach, called adaptive Bayesian inference system (BasIS), uses a probabilistic formulation with a sequential analysis method, for recognition of walking activities performed by participants. Recognition of gait events, needed to identify the state of the human body during the walking activity, is also provided by the proposed method. In addition, the BasIS system includes an adaptive action-perception method for the prediction of gait events. The adaptive approach uses the knowledge gained from decisions made over time by the inference system. The actionperception method allows the BasIS system to autonomously adapt its performance, based on the evaluation of its own predictions and decisions made over time. The proposed approach is implemented in a layered architecture and validated with the recognition of three walking activities; level-ground, ramp ascent and ramp descent. The validation process employs real data from three inertial measurements units attached to the thigh, shanks and foot of participants while performing walking activities. The experiments show that mean decision times of 240 ms and 40 ms are needed to achieve mean accuracies of 99.87% and 99.82% for recognition of walking activities and gait events, respectively. The validation experiments also show that the performance, in accuracy and speed, is not significantly affected when noise is added to sensor measurements. These results show that the proposed adaptive recognition system is accurate, fast and robust to sensor noise, but also capable to adapt its own performance over time. Overall, the adaptive BasIS system demonstrates to be a robust and suitable computational approach for the intelligent recognition of activities of daily living using wearable sensors

A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors

A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots

A model identification approach to quantify impact of whole-body vertical vibrations on limb compliant dynamics and walking stability

Extensive research is ongoing in the field of orthoses/exoskeleton design for efficient lower limbs assistance. However, despite wearable devices reported to improve lower limb mobility, their structural impacts on whole-body vertical dynamics have not been investigated. This study introduced a model identification approach and frequency domain analysis to quantify the impacts of orthosis-generated vibrations on limb stability and contractile dynamics. Experiments were recorded in the motion capture lab using 11 unimpaired subjects by wearing an adjustable ankle–foot orthosis (AFO). The lower limb musculoskeletal structure was identified as spring-mass (SM) and spring-mass-damper (SMD) based compliant models using the whole-body centre-of-mass acceleration data. Furthermore, Nyquist and Bode methods were implemented to quantify stabilities resulting from vertical impacts. Our results illustrated a significant decrease (p < 0.05) in lower limb contractile properties by wearing AFO compared with a normal walk. Also, stability margins quantified by wearing AFO illustrated a significant variance in terms of gain-margins (p < 0.05) for both loading and unloading phases whereas phase-margins decreased (p < 0.05) only for the respective unloading phases. The methods introduced here provide evidence that wearable orthoses significantly affect lower limb vertical dynamics and should be considered when evaluating orthosis/prosthesis/exoskeleton effectiveness

Walking Activity Recognition with sEMG Sensor Array on Thigh Circumference using Convolutional Neural Network

In recognition of walking gait modes using surface electromyography (sEMG), the use of sEMG sensor array can provide sensor redundancy and less rigorous identification of sEMG electrode placements as compared to the conventional sEMG electrode placements right in the middle of muscle bellies. However, the potentially lesser discriminative and noisier sEMG signals from the sEMG sensor array pose the challenge in developing accurate and robust machine learning classifier for walking activity recognition. In this paper, we explore the use of convolution neural network (CNN) classifier with frequency gradient feature derived from EMG signal spectrogram for detecting different walking activities using an sEMG sensor array on thigh circumference. EMG dataset from five healthy subjects and an amputee for five walking activities namely walking at slow, normal and fast speed, ramp ascending and ramp descending are used to train and test the CNN-based classifier. Our preliminary findings suggest that frequency gradient feature can improve the CNN-based classifier performance for walking activity recognition using EMG sensor array on thigh circumference

Stereology shows that damaged liver recovers after protein refeeding

Objective: The aim of the present study was to investigate the putative effects of a low-protein diet on the three-dimensional structure of hepatocytes and determine whether this scenario could be reversed by restoring the adequate levels of protein to the diet. Methods: Using design-based stereology, the total number and volume of hepatocytes were estimated in the liver of mice in healthy and altered (by protein malnutrition) conditions and after protein renutrition. Results: This study demonstrated a 65% decrease in the liver volume (3302 mm3 for the control for undernourished versus 1141 mm3 for the undernourished group) accompanied by a 46% reduction in the hepatocyte volume (8223 μm3 for the control for undernourished versus 4475 μm3 for the undernourished group) and a 90% increase in the total number of binucleate hepatocytes (1 549 393 for the control for undernourished versus 2 941 353 for the undernourished group). Reinstating a normoproteinic diet (12% casein) proved to be effective in restoring the size of hepatocytes, leading to an 85% increase in the total number of uninucleate hepatocytes (15 988 560 for the undernourished versus 29 600 520 for the renourished group), and partially reversed the liver atrophy. Conclusions: Awareness of these data will add to a better morphologic understanding of malnutrition-induced hepatopathies and will help clinicians improve the diagnosis and treatment of this condition in humans and in veterinary practice

Towards an intelligent wearable ankle robot for assistance to foot drop

A wearable ankle robot prototype for assistance to foot drop is presented in this work. This device is built with soft and hard materials and employs one inertial sensor. First, the ankle robot uses a high-level method, developed with a Bayesian formulation, for recognition of walking activities and gait periods. Second, a low-level method, with a proportional-integral-derivative controller (PID), controls the wearable device to operate in assistive and transparent modes. In an assistive mode, activated by the toe-off detection, the wearable device assists the human foot in dorsiflexion orientation to reduce the effect of foot drop abnormality. In a transparent mode, activated by the heel-contact detection, the robot device follows the movements performed by the human foot. The wearable prototype is validated with experiments, in simulation and real-time modes, for recognition of walking activity and control of assistive and transparent modes during walking. Experiments achieved 99.87% and 99.20% accuracies for recognition of walking activity and gait periods. Results also show the ability of the wearable robot to operate according to the gait period recognised during walking. Overall, this work offers a wearable robot prototype with the potential to assist the human foot during walking, which is important to allow subjects to recover their confidence and quality of life

A Practical Gait Feedback Method Based on Wearable Inertial Sensors for a Drop Foot Assistance Device

To maximise the efficiency of gait interventions, gait phase and joint kinematics are important for closing the system loop of adaptive robotic control. However, few studies have applied an inertial sensor system including both gait phase detection and joint kinematic measurement. Many algorithms for joint measurement require careful alignment of the inertial measurement unit (IMU) to the body segment. In this paper, we propose a practical gait feedback method, which provides sufficient feedback without requiring precise alignment of the IMUs. The method incorporates a two-layer model to realise simultaneous gait stance and swing phase detection and ankle joint angle measurement. Recognition of gait phases is performed by a high-level probabilistic method using angular rate from the sensor attached to the shank while the ankle angle is calculated using a data fusion algorithm based on the complementary filter and sensor-to-segment calibration. The online performance of the algorithm was experimentally validated when 10 able-bodied participants walked on the treadmill with three different speeds. The outputs were compared to the ones measured by an optical motion analysis system. The results showed that the IMU-based algorithm achieved a good accuracy of the gait phase recognition (above 95%) with a short delay response below 20 ms and accurate angle measurements with root mean square errors below 3.5° compared to the optical reference. It demonstrates that our method can be used to provide gait feedback for the correction of drop foot