Muscular activity and physical interaction forces during lower limb exoskeleton use

A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton

Locomotion mode recognition in humans is fundamental for flexible control in wearable-powered exoskeleton robots. This article proposes a hybrid model that combines a dense convolutional network (DenseNet) and long short-term memory (LSTM) with a channel attention mechanism (SENet) for locomotion mode recognition. DenseNet can automatically extract deep-level features from data, while LSTM effectively captures long-dependent information in time series. To evaluate the validity of the hybrid model, inertial measurement units (IMUs) and pressure sensors were used to obtain motion data from 15 subjects. Five locomotion modes were tested for the hybrid model, such as level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending. Furthermore, the data features of the ramp were inconspicuous, leading to large recognition errors. To address this challenge, the SENet module was incorporated, which improved recognition rates to some extent. The proposed model automatically extracted the features and achieved an average recognition rate of 97.93%. Compared with known algorithms, the proposed model has substantial recognition results and robustness. This work holds promising potential for applications such as limb support and weight bearing.

Human-Robot Joint Misalignment, Physical Interaction, and Gait Kinematic Assessment in Ankle-Foot Orthoses

Lower limb exoskeletons and orthoses have been increasingly used to assist the user during gait rehabilitation through torque transmission and motor stability. However, the physical human-robot interface (HRi) has not been properly addressed. Current orthoses lead to spurious forces at the HRi that cause adverse effects and high abandonment rates. This study aims to assess and compare, in a holistic approach, human-robot joint misalignment and gait kinematics in three fixation designs of ankle-foot orthoses (AFOs). These are AFOs with a frontal shin guard (F-AFO), lateral shin guard (L-AFO), and the ankle modulus of the H2 exoskeleton (H2-AFO). An experimental protocol was implemented to assess misalignment, fixation displacement, pressure interactions, user-perceived comfort, and gait kinematics during walking with the three AFOs. The F-AFO showed reduced vertical misalignment (peak of 1.37 ± 0.90 cm, p-value < 0.05), interactions (median pressures of 0.39-3.12 kPa), and higher user-perceived comfort (p-value < 0.05) when compared to H2-AFO (peak misalignment of 2.95 ± 0.64 and pressures ranging from 3.19 to 19.78 kPa). F-AFO also improves the L-AFO in pressure (median pressures ranging from 8.64 to 10.83 kPa) and comfort (p-value < 0.05). All AFOs significantly modified hip joint angle regarding control gait (p-value < 0.01), while the H2-AFO also affected knee joint angle (p-value < 0.01) and gait spatiotemporal parameters (p-value < 0.05). Overall, findings indicate that an AFO with a frontal shin guard and a sports shoe is effective at reducing misalignment and pressure at the HRI, increasing comfort with slight changes in gait kinematics.

Relevance of hazards in exoskeleton applications: a survey-based enquiry

Exoskeletons are becoming the reference technology for assistance and augmentation of human motor functions in a wide range of application domains. Unfortunately, the exponential growth of this sector has not been accompanied by a rigorous risk assessment (RA) process, which is necessary to identify the major aspects concerning the safety and impact of this new technology on humans. This situation may seriously hamper the market uptake of new products. This paper presents the results of a survey that was circulated to understand how hazards are considered by exoskeleton users, from research and industry perspectives. Our analysis aimed to identify the perceived occurrence and the impact of a sample of generic hazards, as well as to collect suggestions and general opinions from the respondents that can serve as a reference for more targeted RA. Our results identified a list of relevant hazards for exoskeletons. Among them, misalignments and unintended device motion were perceived as key aspects for exoskeletons' safety. This survey aims to represent a first attempt in recording overall feedback from the community and contribute to future RAs and the identification of better mitigation strategies in the field.

A CNN–SVM MODEL USING IMU FOR LOCOMOTION MODE RECOGNITION IN LOWER EXTREMITY EXOSKELETON

Human activity intention is indispensable for wearable powered lower extremity exoskeleton such that ensuring the compliant control of the robot. Lots of researches have been done on gait phase detection, which served as a sub-module of locomotion mode recognition to support the follow-on task. Therefore, it is self-evident that locomotion mode recognition is of great importance. Many model-based recognition methods are usually applied in manual extraction of cumbersome features, such as the traditional neural network (NN), support vector machine (SVM), etc. In contrast, the feature mapping layer coming with the convolutional neural network (CNN) can effectively solve the above time-consuming problem. Given that the training of NN is prone to overfitting, SVM with optimal characteristics is considered. A hybrid CNN–SVM model is proposed to identify human locomotion modes by collecting multi-channel inertial measurement unit (IMU) signals and is integrated with the error correction function of the finite state machine (FSM). Therefore, the CNN–SVM model has great influence on the generalization performance and recognition accuracy. The recognition rates of five single locomotion modes and eight mixed locomotion modes reach 97.91% and 98.93%, respectively. The system meets the demand of real-time performance, and the recognition time exceeds 370[Formula: see text]ms on heel strike.

Characterization and Evaluation of Human-Exoskeleton Interaction Dynamics: A Review

Exoskeletons and exosuits have witnessed unprecedented growth in recent years, especially in the medical and industrial sectors. In order to be successfully integrated into the current society, these devices must comply with several commercialization rules and safety standards. Due to their intrinsic coupling with human limbs, one of the main challenges is to test and prove the quality of physical interaction with humans. However, the study of physical human-exoskeleton interactions (pHEI) has been poorly addressed in the literature. Understanding and identifying the technological ways to assess pHEI is necessary for the future acceptance and large-scale use of these devices. The harmonization of these evaluation processes represents a key factor in building a still missing accepted framework to inform human-device contact safety. In this review, we identify, analyze, and discuss the metrics, testing procedures, and measurement devices used to assess pHEI in the last ten years. Furthermore, we discuss the role of pHEI in safety contact evaluation. We found a very heterogeneous panorama in terms of sensors and testing methods, which are still far from considering realistic conditions and use-cases. We identified the main gaps and drawbacks of current approaches, pointing towards a number of promising research directions. This review aspires to help the wearable robotics community find agreements on interaction quality and safety assessment testing procedures.

Improved Motion Classification With an Integrated Multimodal Exoskeleton Interface

Human motion intention detection is an essential part of the control of upper-body exoskeletons. While surface electromyography (sEMG)-based systems may be able to provide anticipatory control, they typically require exact placement of the electrodes on the muscle bodies which limits the practical use and donning of the technology. In this study, we propose a novel physical interface for exoskeletons with integrated sEMG- and pressure sensors. The sensors are 3D-printed with flexible, conductive materials and allow multi-modal information to be obtained during operation. A K-Nearest Neighbours classifier is implemented in an off-line manner to detect reaching movements and lifting tasks that represent daily activities of industrial workers. The performance of the classifier is validated through repeated experiments and compared to a unimodal EMG-based classifier. The results indicate that excellent prediction performance can be obtained, even with a minimal amount of sEMG electrodes and without specific placement of the electrode.

Analysis of Human-Exoskeleton System Interaction for Ergonomic Design

OBJECTIVE

Lower-limb exoskeleton systems are defined as gait training or walking-assisting devices for spinal cord injury or hemiplegic patients. Crutches, straps, and baffles are designed to protect subjects from falling. However, skin abrasions occur when the interaction forces are too large. In this study, the interaction forces between the human body and an exoskeleton system named the AIDER were measured to confirm whether the design was ergonomic.

BACKGROUND

The AIDER system is a wearable lower-limb exoskeleton. It secures a subject by binding on the waist, thighs, shanks, and feet.

METHOD

Eight healthy subjects participated in the study. The interaction forces of the waist strap, thigh baffles, shank baffles, and crutch handles were measured by pressure sensors. Ten repetitions were completed in this study. After one repetition, custom comfort questionnaires were completed by the subjects.

RESULTS

Although a few of the peak values of the maximum intensities of pressure between the hands and crutch handles reached the minimum value of the pain-pressure threshold (PPT), the average pressure intensities were much smaller than the PPT value.

CONCLUSIONS

The results indicated that the mechanical structure and control strategy of the AIDER must be improved to be more ergonomic in the future.

Monitoring Contact Behavior During Assisted Walking With a Lower Limb Exoskeleton

Interaction forces between a robotic cuff and the skin during assisted walking with a lower limb exoskeleton may cause skin injuries over time, such as blisters and ulcers. This study proposes a sensing cuff that can monitor the contact behavior between the exoskeleton and skin during assisted walking, and a functional test and assisted walking experiments were conducted to evaluate the performance of the proposed device. The functional test of the sensing cuff showed good performance of capturing the contact behavior for safety evaluation. The walking experiment involved subjects walking on a treadmill with a lower limb exoskeleton under different conditions (i.e., walking speed and clothing), and the sensing cuff attached to the exoskeleton measured the interaction forces and slip velocity. The magnitude of shear force in the movement direction peaked near the beginning and within 40 - 50% of the gait cycle. The contact safety of the lower limb exoskeleton during assisted walking was then evaluated based on the calculated shear stress. The designed sensing cuff could provide sufficient information regarding contact behavior and contact safety during assisted walking.