Toward Minimal-Sensing Locomotion Mode Recognition for a Powered Knee-Ankle Prosthesis

Visual Illusion Created by a Striped Pattern Through Augmented Reality for the Prevention of Tumbling on Stairs

A fall on stairs can be a dangerous accident. An important indicator of falling risk is the foot clearance, which is the height of the foot when ascending stairs or the distance of the foot from the step when descending. We developed an augmented reality system with a holographic lens using a visual illusion to improve the foot clearance on stairs. The system draws a vertical striped pattern on the stair riser as the participant ascends the stairs to create the illusion that the steps are higher than the actual steps, and draws a horizontal striped pattern on the stair tread as the participant descends the stairs to create the illusion of narrower stairs. We experimentally evaluated the accuracy of the system and fitted a model to determine the appropriate stripe thickness. Finally, participants ascended and descended stairs before, during, and after using the augmented reality system. The foot clearance significantly improved, not only while the participants used the system but also after they used the system compared with before.

The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development

Advancements in instrumentation support improved powered ankle prostheses hardware development. However, control algorithms have limitations regarding number and type of sensors utilized and achieving autonomous adaptation, which is key to a natural ambulation. Surface electromyogram (sEMG) sensors are promising. With a minimized number of sEMG inputs an economic control algorithm can be developed, whereas limiting the use of lower leg muscles will provide a practical algorithm for both ankle disarticulation and transtibial amputation. To determine appropriate sensor combinations, a systematic assessment of the predictive success of variations of multiple sEMG inputs in estimating ankle position and moment has to conducted. More importantly, tackling the use of nonnormalized sEMG data in such algorithm development to overcome processing complexities in real-time is essential, but lacking. We used healthy population level walking data to (1) develop sagittal ankle position and moment predicting algorithms using nonnormalized sEMG, and (2) rank all muscle combinations based on success to determine economic and practical algorithms. Eight lower extremity muscles were studied as sEMG inputs to a long-short-term memory (LSTM) neural network architecture: tibialis anterior (TA), soleus (SO), medial gastrocnemius (MG), peroneus longus (PL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and gluteus maximus (GMax). Five features extracted from nonnormalized sEMG amplitudes were used: integrated EMG (IEMG), mean absolute value (MAV), Willison amplitude (WAMP), root mean square (RMS) and waveform length (WL). Muscle and feature combination variations were ranked using Pearson's correlation coefficient (r > 0.90 indicates successful correlations), the root-mean-square error and one-dimensional statistical parametric mapping between the original data and LSTM response. The results showed that IEMG+WL yields the best feature combination performance. The best performing variation was MG + RF + VM (r_position = 0.9099 and r_moment = 0.9707) whereas, PL (r_position = 0.9001, r_moment = 0.9703) and GMax+VM (r_position = 0.9010, r_moment = 0.9718) were distinguished as the economic and practical variations, respectively. The study established for the first time the use of nonnormalized sEMG in control algorithm development for level walking.

Low limb prostheses and complex human prosthetic interaction: A systematic literature review

A few years ago, powered prostheses triggered new technological advances in diverse areas such as mobility, comfort, and design, which have been essential to improving the quality of life of individuals with lower limb disability. The human body is a complex system involving mental and physical health, meaning a dependant relationship between its organs and lifestyle. The elements used in the design of these prostheses are critical and related to lower limb amputation level, user morphology and human-prosthetic interaction. Hence, several technologies have been employed to accomplish the end user's needs, for example, advanced materials, control systems, electronics, energy management, signal processing, and artificial intelligence. This paper presents a systematic literature review on such technologies, to identify the latest advances, challenges, and opportunities in developing lower limb prostheses with the analysis on the most significant papers. Powered prostheses for walking in different terrains were illustrated and examined, with the kind of movement the device should perform by considering the electronics, automatic control, and energy efficiency. Results show a lack of a specific and generalised structure to be followed by new developments, gaps in energy management and improved smoother patient interaction. Additionally, Human Prosthetic Interaction (HPI) is a term introduced in this paper since no other research has integrated this interaction in communication between the artificial limb and the end-user. The main goal of this paper is to provide, with the found evidence, a set of steps and components to be followed by new researchers and experts looking to improve knowledge in this field.

Exploring the Utility of Crutch Force Sensors to Predict User Intent in Assistive Lower Limb Exoskeletons

The adoption of assistive lower limb exoskeletons in built environments is reliant on the further development of these devices to handle the varied conditions experienced in everyday life. The required development includes more varied and flexible gait patterns, but also appropriate user interfaces to enable fluid gait. This work explores the properties of an algorithm used to predict user intent based on sensors onboard a user-balanced robotic exoskeleton system. Specifically, classification algorithms built with different input data sets are compared - with varying detail of the interaction forces between the crutches and the ground, and the duration of the data sample used to make the prediction. Data were collected with one able-bodied participant using an exoskeleton, training three independent classifiers corresponding to different exoskeleton states. The results indicate the value of including information about the interaction forces between the crutches and the ground in improving prediction accuracy, with increasing prediction window also generally resulting in an increase in prediction accuracy. Whilst no categorical recommendation can be made with respect to either parameter, these results provide a baseline which can be used in conjunction deliberate consideration of the costs associated with implementation.

Estimation of knee and ankle angles during walking using thigh and shank angles

Estimation of joints' trajectories is commonly used in human gait analysis, and in the development of motion planners and high-level controllers for prosthetics, orthotics, exoskeletons and humanoids. Human locomotion is the result of the cooperation between leg joints and limbs. This suggests the existence of underlying relationships between them which lead to a harmonic gait. In this study we aimed to estimate knee and ankle trajectories using thigh and shank angles. To do so, an estimation approach was developed that continuously mapped the inputs to the outputs, which did not require switching rules, speed estimation, gait percent identification or look-up tables. The estimation algorithm was based on a nonlinear auto-regressive model with exogenous inputs. The method was then combined with wavelets theory, and then the two were used in a neural network. To evaluate the estimation performance, three scenarios were developed which used only one source of inputs (i.e., only shank angles or only thigh angles). First, knee anglesθ_k(outputs) were estimated using thigh anglesθ_th(inputs). Second, ankle anglesθ_a(outputs) were estimated using thigh anglesθ_sh(inputs), and third, the ankle angles were estimated using shank angles (inputs). The proposed approach was investigated for 22 subjects at different walking speeds and the leave-one-subject-out procedure was used for training and testing the estimation algorithm. Average root mean square errors were 3.9°-5.3° and 2.1°-2.3° for knee and ankle angles, respectively. Average mean absolute errors (MAEs) MAEs were 3.2°-4° and 1.7°-1.8°, and average correlation coefficientsρ_ccwere 0.95-0.98 and 0.94-0.96 for knee and ankle angles, respectively. The limitations and strengths of the proposed approach are discussed in detail and the results are compared with several studies.

Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man-machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.