The analysis of aerobics intelligent fitness system for neurorobotics based on big data and machine learning

In modern society, people's pace of life is fast, and the pressure is enormous, leading to increasingly prominent issues such as obesity and sub-health. Traditional fitness methods cannot meet people's needs to a certain extent. Therefore, this work aims to use technology to change people's lifestyles and compensate for traditional fitness methods' shortcomings. Firstly, this work overviews neurorobotics, providing neural perception and control functions for aerobics intelligent fitness system. Secondly, the connection between big data and machine learning (ML), big data technology products, and the ML process are discussed. The Spark big data platform builds node data for calculation, and the decision tree algorithm is used for data preprocessing. These are important for future intelligent fitness analysis. This work proposes an aerobics intelligent fitness system based on neurorobotics technology and big data analysis and develops a recommendation system for the best fitness exercise. This system utilizes neural perception and control functions, combined with big data and ML technology, to solve the obesity and sub-health problems faced by people in fast-paced and high-pressure lifestyles. By harnessing the computational capabilities of the Spark big data platform and applying the decision tree algorithm for data preprocessing, the system can furnish users with personalized fitness plans and optimization recommendations. This work conducts a model performance study on 35 % aerobic fitness data on intelligent fitness Android v1.0.8 to evaluate the system's data processing ability and training effectiveness. Moreover, the aerobics intelligent fitness system models based on neurorobotics, big data, and ML are evaluated. The results indicate that normalizing the data using the Min-Max method leads to a decrease in the F1 value and a reduction in data set errors. Consequently, the dataset studied by the system model is beneficial to improving the work efficiency of the aerobics intelligent fitness system. After the comprehensive human quality of the system model is evaluated, the actual average score of the comprehensive human quality of the 13 users tested before the aerobics intelligent fitness system test is 91.44, and the average prediction score is 90.88. The results of the two tests are similar. Thus, using the intelligent fitness system can enable the user to obtain system feedback according to the actual training effect, thereby playing a guiding role in the intelligent fitness of aerobics for the user. This work designs and implements the aerobics intelligent fitness system close to the human body's training effect, further enhancing the specialization and individualization of sports and fitness.

Estimating human joint moments unifies exoskeleton control, reducing user effort

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

User- and Speed-Independent Slope Estimation for Lower-Extremity Wearable Robots

Wearable robots can help users traverse unstructured slopes by providing mode-specific hip, knee, and ankle joint assistance. However, generalizing the same assistance pattern across different slopes is not optimal. Control strategies that scale assistance based on slope are expected to improve the feel of the device and improve outcome measures such as decreasing metabolic cost. Prior numerical methods for slope estimation struggled to estimate slopes at variable walking speeds or were limited to a single estimation per gait cycle. This study overcomes these limitations by developing machine-learning methods that yield continuous, user- and speed-independent slope estimators for a variety of wearable robot applications using an able-bodied wearable sensor dataset. In a leave-one-subject-out cross-validation (N = 9), four-phase XGBoost regression models were trained on static-slope (fixed-slope) data and evaluated on a novel subject's static-slope and dynamic-slope (variable-slope) data. Using all available sensors, we achieved an average error of 0.88° and 1.73° mean absolute error (MAE) on static and dynamic slopes, respectively. Ankle prosthesis, knee-ankle prosthesis, and hip exoskeleton sensor suites yielded average errors under 2° MAE on static and dynamic slopes, except for the ankle prosthesis and hip exoskeleton cases on dynamic slopes which yielded an average error of 2.2° and 3.2° MAE, respectively. We found that the thigh inertial measurement unit contributed the most to a reduction in average error. Our findings suggest that reliable slope estimators can be trained using only static-slope data regardless of the type of lower-extremity wearable robot.

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.

Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters

Emerging partial-assistance exoskeletons can enhance able-bodied performance and aid people with pathological gait or age-related immobility. However, every person walks differently, which makes it difficult to directly compute assistance torques from joint kinematics. Gait-state estimation-based controllers use phase (normalized stride time) and task variables (e.g., stride length and ground inclination) to parameterize the joint torques. Using kinematic models that depend on the gait-state, prior work has used an Extended Kalman filter (EKF) to estimate the gait-state online. However, this EKF suffered from kinematic errors since it used a subject-independent measurement model, and it is still unknown how personalization of this measurement model would reduce gait-state tracking error. This paper quantifies how much gait-state tracking improvement a personalized measurement model can have over a subject-independent measurement model when using an EKF-based gait-state estimator. Since the EKF performance depends on the measurement model covariance matrix, we tested on multiple different tuning parameters. Across reasonable values of tuning parameters that resulted in good performance, personalization improved estimation error on average by 8.5 ± 13.8% for phase (mean ± standard deviation), 27.2 ± 8.1% for stride length, and 10.5 ± 13.5% for ground inclination. These findings support the hypothesis that personalization of the measurement model significantly improves gait-state estimation performance in EKF based gait-state tracking (P ≪ 0.05 ), which could ultimately enable reliable responses to faster human gait changes.

The Effects of Incline Level on Optimized Lower-Limb Exoskeleton Assistance: a Case Series

For exoskeletons to be successful in real-world settings, they will need to be effective across a variety of terrains, including on inclines. While some single-joint exoskeletons have assisted incline walking, recent successes in level-ground assistance suggest that greater improvements may be possible by optimizing assistance of the whole leg. To understand how exoskeleton assistance should change with incline, we used human-in-the-loop optimization to find whole-leg exoskeleton assistance torques that minimized metabolic cost on a range of grades. We optimized assistance for three able-bodied, expert participants on 5 degree, 10 degree, and 15 degree inclines using a hip-knee-ankle exoskeleton emulator. For all assisted conditions, the cost of transport was reduced by at least 50% relative to walking in the device with no assistance, which is a large improvement to walking comparable to the benefits of whole-leg assistance on level-ground (N = 3). Optimized extension torque magnitudes and exoskeleton power increased with incline. Hip extension, knee extension and ankle plantarflexion often grew as large as allowed by comfort-based limits. Applied powers on steep inclines were double the powers applied during level-ground walking, indicating that greater exoskeleton power may be optimal in scenarios where biological powers and costs are higher. Future exoskeleton devices could deliver large improvements in walking performance across a range of inclines if they have sufficient torque and power capabilities.

Current developments of robotic hip exoskeleton toward sensing, decision, and actuation: A review

The aging population is now a global challenge, and impaired walking ability is a common feature in the elderly. In addition, some occupations such as military and relief workers require extra physical help to perform tasks efficiently. Robotic hip exoskeletons can support ambulatory functions in the elderly and augment human performance in healthy people during normal walking and loaded walking by providing assistive torque. In this review, the current development of robotic hip exoskeletons is presented. In addition, the framework of actuation joints and the high-level control strategy (including the sensors and data collection, the way to recognize gait phase, the algorithms to generate the assist torque) are described. The exoskeleton prototypes proposed by researchers in recent years are organized to benefit the related fields realizing the limitations of the available robotic hip exoskeletons, therefore, this work tends to be an influential factor with a better understanding of the development and state-of-the-art technology.

Analysis of the Bayesian Gait-State Estimation Problem for Lower-Limb Wearable Robot Sensor Configurations

Many exoskeletons today are primarily tested in controlled, steady-state laboratory conditions that are unrealistic representations of their real-world usage in which walking conditions (e.g., speed, slope, and stride length) change constantly. One potential solution is to detect these changing walking conditions online using Bayesian state estimation to deliver assistance that continuously adapts to the wearer's gait. This paper investigates such an approach in silico, aiming to understand 1) which of the various Bayesian filter assumptions best match the problem, and 2) which gait parameters can be feasibly estimated with different combinations of sensors available to different exoskeleton configurations (pelvis, thigh, shank, and/or foot). Our results suggest that the assumptions of the Extended Kalman Filter are well suited to accurately estimate phase, stride frequency, stride length, and ramp inclination with a wide variety of sparse sensor configurations.