Ramp Ascent Research Articles

Comparing the lower limb joint biomechanics of the Power Knee, C-Leg and Rheo Knee during ramp and stair ambulation

One of the most significant developments in prosthetic knee technology has been the introduction of the Microprocessor-Controlled Prosthetic Knee (MPK). However, there is a lack of consensus over how different types of MPKs affect performance in different ambulation modes. In this study, we investigated the biomechanical differences in ramp and stair maneuvers when an individual with transfemoral amputation wears three commercial MPKs: the Össur Power Knee, the Össur Rheo Knee and the Ottobock C-Leg 4. The primary outcome variable for this study was the lower limb biological joint work, inclusive of the intact leg and prosthetic side hip. We hypothesized that (1) the Power Knee would result in lower biological work during ascent activities than the C-Leg and Rheo, both passive MPKs, and (2) the C-Leg and Rheo would result in lower biological work during descent activities than the Power Knee. During ramp ascent, the C-Leg was associated with lower biological joint work (p < 0.05) than the Power Knee. However, this relationship did not hold during stair ascent, where the Power Knee showed advantages for stair ascent with net reductions in biological joint work of 14.1% and 23.3% compared to the Rheo and C-leg, respectively. There were no significant differences in biological joint work between the knees during ramp and stair descent, indicating that choice of MPK may not be as important for descent activities. Our results demonstrate that differences are present between different types of MPKs during ascent activities which could prove useful in the prescription of these devices.

EMG and IMU Data Fusion for Locomotion Mode Classification in Transtibial Amputees

Despite recent advancements in prosthetic technology, lower-limb amputees often remain limited to passive prostheses, which leads to an asymmetric gait and increased energy expenditure. Developing active prostheses with effective control systems is important to improve mobility for these individuals. This study presents a machine-learning-based approach to classify five distinct locomotion tasks: ground-level walking (GWL), ramp ascent (RPA), ramp descent (RPD), stairs ascent (SSA), and stairs descent (SSD). The dataset comprises fused electromyographic (EMG) and inertial measurement unit (IMU) signals from twenty non-amputated and five transtibial amputated participants. EMG sensors were strategically positioned on the thigh muscles, while IMU sensors were placed on various leg segments. The performance of two classification algorithms, support vector machine (SVM) and long short-term memory (LSTM), were evaluated on segmented data. The results indicate that SVM models outperform LSTM models in accuracy, precision, and F1 score in the individual evaluation of amputee and non-amputee datasets for 80–20 and 50–50 data distributions. In the 80–20 distribution, an accuracy of 95.46% and 95.35% was obtained with SVM for non-amputees and amputees, respectively. An accuracy of 93.33% and 93.30% was obtained for non-amputees and amputees by using LSTM, respectively. LSTM models show more robustness and inter-population generalizability than SVM models when applying domain-adaptation techniques. Furthermore, the average classification latency for SVM and LSTM models was 19.84 ms and 37.07 ms, respectively, within acceptable limits for real-time applications. This study contributes to the field by comprehensively comparing SVM and LSTM classifiers for locomotion tasks, laying the foundation for the future development of real-time control systems for active transtibial prostheses.

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors.

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard' to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.

BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors.

Through wearable sensors and deep learning techniques, biomechanical analysis can reach beyond the lab for clinical and sporting applications. Transformers, a class of recent deep learning models, have become widely used in state-of-the-art artificial intelligence research due to their superior performance in various natural language processing and computer vision tasks. The performance of transformer models has not yet been investigated in biomechanics applications. In this study, we introduce a Biomechanical Multi-activity Transformer-based model, BioMAT, for the estimation of joint kinematics from streaming signals of multiple inertia measurement units (IMUs) using a publicly available dataset. This dataset includes IMU signals and the corresponding sagittal plane kinematics of the hip, knee, and ankle joints during multiple activities of daily living. We evaluated the model's performance and generalizability and compared it against a convolutional neural network long short-term model, a bidirectional long short-term model, and multi-linear regression across different ambulation tasks including level ground walking (LW), ramp ascent (RA), ramp descent (RD), stair ascent (SA), and stair descent (SD). To investigate the effect of different activity datasets on prediction accuracy, we compared the performance of a universal model trained on all activities against task-specific models trained on individual tasks. When the models were tested on three unseen subjects' data, BioMAT outperformed the benchmark models with an average root mean square error (RMSE) of 5.5 ± 0.5°, and normalized RMSE of 6.8 ± 0.3° across all three joints and all activities. A unified BioMAT model demonstrated superior performance compared to individual task-specific models across four of five activities. The RMSE values from the universal model for LW, RA, RD, SA, and SD activities were 5.0 ± 1.5°, 6.2 ± 1.1°, 5.8 ± 1.1°, 5.3 ± 1.6°, and 5.2 ± 0.7° while these values for task-specific models were, 5.3 ± 2.1°, 6.7 ± 2.0°, 6.9 ± 2.2°, 4.9 ± 1.4°, and 5.6 ± 1.3°, respectively. Overall, BioMAT accurately estimated joint kinematics relative to previous machine learning algorithms across different activities directly from the sequence of IMUs signals instead of time-normalized gait cycle data.

Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis

Intelligent lower-limb prosthesis appears in the public view due to its attractive and potential functions, which can help amputees restore mobility and return to normal life. To realize the natural transition of locomotion modes, locomotion mode classification is the top priority. There are mainly five steady-state and periodic motions, including LW (level walking), SA (stair ascent), SD (stair descent), RA (ramp ascent), and RD (ramp descent), while ST (standing) can also be regarded as one locomotion mode (at the start or end of walking). This paper mainly proposes four novel features, including TPDS (thigh phase diagram shape), KAT (knee angle trajectory), CPO (center position offset) and GRFPV (ground reaction force peak value) and designs ST classifier and artificial neural network (ANN) classifier by using a user-dependent dataset to classify six locomotion modes. Gaussian distributions are applied in those features to simulate the uncertainty and change of human gaits. An angular velocity threshold and GRFPV feature are used in the ST classifier, and the artificial neural network (ANN) classifier explores the mapping relation between our features and the locomotion modes. The results show that the proposed method can reach a high accuracy of 99.16% ± 0.38%. The proposed method can provide accurate motion intent of amputees to the controller and greatly improve the safety performance of intelligent lower-limb prostheses. The simple structure of ANN applied in this paper makes adaptive online learning algorithms possible in the future.

Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.

Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuit consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial-temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of 91.8±3.4%, compared with 93.8±3.0%, when using kinematic data alone. Combined kinematic and ultrasound produced 95.8±2.3% accuracy. This suggests that A-mode ultrasound provides additional useful information about the user's gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.

Flexible Capacitive Sensing and Ultrasound Calibration for Skeletal Muscle Deformations.

Skeletal muscles are critical to human-limb motion dynamics and energetics, where their mechanical states are seldom explored in vitro due to practical limitations of sensing technologies. This article aims to capture mechanical deformations of muscle contraction using wearable flexible sensors, which is justified with model calibration and experimental validation. The capacitive sensor is designed with the composite of conductive fabric electrodes and the porous dielectric layer to increase the pressure sensitivity and prevent lateral expansions. In this way, the compressive displacement of muscle deformation is captured in the muscle-sensor coupling model in terms of sensor deformation and parameters of pretension, material, and shape properties. The sensing model is calibrated in a linear form using ultrasound medical imaging. The sensor is capable of measuring muscle strain of 70% with an error of <3.6% and temperature disturbance of <5.6%. After 10K cycles of compression, the drift is only 3.3%. Immediate application of the proposed method is illustrated by gait pattern identification, where the K-nearest neighbor prediction accuracy of squats, level walking, stair ascent/descent, and ramp ascent is over 97% with a standard deviation below 2.6% compared to that of 94.61 ± 4.24% for ramp descent, and the response time is 14.37 ± 0.52 ms. The wearable sensing method is valid for muscle deformation monitoring and gait pattern identification, and it provides an alternative approach to capture mechanical motions of muscles, which is anticipated to contribute to understand locomotion biomechanics in terms of muscle forces and metabolic landscapes.

Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking.

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.

IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks.

This paper focuses on the classification of seven locomotion modes (sitting, standing, level ground walking, ramp ascent and descent, stair ascent and descent), the transitions among these modes, and the gait phases within each mode, by only using data in the frequency domain from one or two inertial measurement units. Different deep neural network configurations are investigated and compared by combining convolutional and recurrent layers. The results show that a system composed of a convolutional neural network followed by a long short-term memory network is able to classify with a mean F1-score of 0.89 and 0.91 for ten healthy subjects, and of 0.92 and 0.95 for one osseointegrated transfemoral amputee subject (excluding the gait phases because they are not labeled in the data-set), using one and two inertial measurement units, respectively, with a 5-fold cross-validation. The promising results obtained in this study pave the way for using deep learning for the control of transfemoral prostheses with a minimum number of inertial measurement units.

Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model.

This paper proposes to use deep reinforcement learning to teach a physics-based human musculoskeletal model to ascend stairs and ramps. The deep reinforcement learning architecture employs the proximal policy optimization algorithm combined with imitation learning and is trained with experimental data of a public dataset. The human model is developed in the open-source simulation software OpenSim, together with two objects (i.e., the stairs and ramp) and the elastic foundation contact dynamics. The model can learn to ascend stairs and ramps with muscle forces comparable to healthy subjects and with a forward dynamics comparable to the experimental training data, achieving an average correlation of 0.82 during stair ascent and of 0.58 during ramp ascent across both the knee and ankle joints.

Untethered muscle tracking using magnetomicrometry.

Muscle tissue drives nearly all movement in the animal kingdom, providing power, mobility, and dexterity. Technologies for measuring muscle tissue motion, such as sonomicrometry, fluoromicrometry, and ultrasound, have significantly advanced our understanding of biomechanics. Yet, the field lacks the ability to monitor muscle tissue motion for animal behavior outside the lab. Towards addressing this issue, we previously introduced magnetomicrometry, a method that uses magnetic beads to wirelessly monitor muscle tissue length changes, and we validated magnetomicrometry via tightly-controlled in situ testing. In this study we validate the accuracy of magnetomicrometry against fluoromicrometry during untethered running in an in vivo turkey model. We demonstrate real-time muscle tissue length tracking of the freely-moving turkeys executing various motor activities, including ramp ascent and descent, vertical ascent and descent, and free roaming movement. Given the demonstrated capacity of magnetomicrometry to track muscle movement in untethered animals, we feel that this technique will enable new scientific explorations and an improved understanding of muscle function.

A GMM-DTW-Based Locomotion Mode Recognition Method in Lower Limb Exoskeleton

Exoskeleton robots can provide support and protection to the human body as well as be able to perform a series of tasks under control. Locomotion mode recognition is an essential prerequisite for assisted control of lower limb exoskeleton robots. A system is designed to be applied to a variety of individuals with quick and accurate identification. In this article, we propose a novel real-time locomotion mode recognition algorithm based on Gaussian mixture model (GMM) features aligned by dynamic time warping (DTW). The spatial distribution of the sensor data is represented by the GMM features, which emphasize the coupling between joints. This peculiarity promotes the distinction of similarity judgment and shows superior generalization performance. Furthermore, we expand the study of the warping path. We further validate the reasonableness of the DTW algorithm for feature matching to improve the recognition rate. The data of several inertial measurement units (IMUs) and pressure sensors were collected by 17 subjects in three terrains, including level ground, stairs, and ramp. We define the five locomotion modes as walking on level ground, stair ascent (SA), stair descent, ramp ascent, and ramp descent. Compared with the algorithms in previous papers, the GMM-DTW algorithm can achieve substantial recognition rates and good generalization performance with low latency. This work establishes the groundwork for the soft control of exoskeleton robots in the future.

Modelling and analysis of orthoses generated whole-body vertical vibrations impact on limb stability and compliant dynamics in a ramp gait

Whole-body vibrations provide important proprioceptive feedback for the control of human locomotion. However, these vibrations are also reported to have a detrimental effect on several existing musculoskeletal disorders. Studies have to date reported on the limits of these centre-of-mass (CoM) vibrations mainly for the level walk. However, their impact remains unquantified for other walking situations such as ramp ascent and descent (A/D). Orthotic devices have also been reported as improving limb mobility, but their contribution to whole-body vertical dynamics has not yet been investigated in ramp walking studies. This study presents methods for evaluating normal limits of CoM-vibrations on a ramp, with and without the effect of an ankle–foot orthosis (AFO). Experiments are performed on a force plate and CoM-vibrations data is recorded as a function of impact forces for both normal and AFO walking conditions. These CoM-vibrations are then modelled using a regression technique and analysed in the frequency domain. The normal limits of CoM-vibrations are quantified as limb compliance properties and stability margins for both A/D. A comparison between normal and AFO walking conditions identified significant variations (Friedman-test, p < 0.05) in lower limb compliance dynamics and stability margins. Results also showed the significant impact of AFO adjustments on musculoskeletal dynamics. This preliminary study defines the normal limits of impact forces and their efficiency associated with the wearing of an AFO. Clinically, these outcomes are important in evaluating the deleterious effects of impact forces in patients and on the effectiveness of measures taken to overcome impact forces using assistive devices.

Wearable Iontronic FMG for Classification of Muscular Locomotion.

Human motion recognition with high accuracy and fast response speed has long been considered an essential component in human-machine interactive activities such as assistive robotics, medical prosthesis, and wearable electronics. The force myography (FMG) signal has been the focus of much investigation in the search for a reliable and efficient muscular locomotion recognition system. However, the effect of the sensing system on FMG-based locomotion classification accuracy has yet to be understood. This study proposed a novel FMG sensing strategy for human lower limb locomotion classification based on flexible supercapacitive iontronic sensors. Benefiting from the ultrahigh sensitivity (up to 1 nF/mmHg) and low activation pressure (less than 5 mmHg) of the supercapacitive iontronic pressure sensor, FMG signal can be acquired accurately from 5 iontronic sensors strapped to the thigh. In the experiment with 12 subjects, the real-time classification strategy based on sliding window and SVM model gave an average locomotion classification accuracy of 99% for seven categories, including sitting, standing, walking on level ground, ramp ascent, ramp descent, stair ascent, stair descent. Compared with traditional FSR sensors, the result showed that iontronic sensors improved the classification accuracy by up to 10 percentage points in the case of short time window. The implementation of the high sensitivity flexible iontronic sensors in the wearable system brings a valuable tool for detecting small human body pressure signals and has great potential to improve the performance of the human-machine interface in rehabilitation and medical applications.

Unsupervised Sim-to-Real Adaptation for Environmental Recognition in Assistive Walking.

Powered lower-limb prostheses with vision sensors are expected to restore amputees' mobility in various environments with supervised learning-based environmental recognition. Due to the sim-to-real gap, such as real-world unstructured terrains and the perspective and performance limitations of vision sensor, simulated data cannot meet the requirement for supervised learning. To mitigate this gap, this paper presents an unsupervised sim-to-real adaptation method to accurately classify five common real-world (level ground, stair ascent, stair descent, ramp ascent and ramp descent) and assist amputee's terrain-adaptive locomotion. In this study, augmented simulated environments are generated from a virtual camera perspective to better simulate the real world. Then, unsupervised domain adaptation is incorporated to train the proposed adaptation network consisting of a feature extractor and two classifiers is trained on simulated data and unlabeled real-world data to minimize domain shift between source domain (simulation) and target domain (real world). To interpret the classification mechanism visually, essential features of different terrains extracted by the network are visualized. The classification results in walking experiments indicate that the average accuracy on eight subjects reaches (98.06% ± 0.71 %) and (95.91% ± 1.09 %) in indoor and outdoor environments respectively, which is close to the result of supervised learning using both type of labeled data (98.37% and 97.05%). The promising results demonstrate that the proposed method is expected to realize accurate real-world environmental classification and successful sim-to-real transfer.

Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons.

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors—specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and −5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side’s mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.

Learning architecture for the recognition of walking and prediction of gait period using wearable sensors

This work presents a novel learning architecture for the recognition and prediction of walking activity and gait period, respectively, using wearable sensors. This approach is composed of a Convolutional Neural Network (CNN), a Predicted Information Gain (PIG) module and an adaptive combination of information sources. The CNN provides the recognition of walking and gait periods. This information is used by the proposed PIG method to estimate the next most probable gait period along the gait cycle. The outputs from the CNN and PIG modules are combined by a proposed adaptive process, which relies on data from the source that shows to be more reliable. This adaptive combination ensures that the learning architecture provides accurate recognition and prediction of walking activity and gait periods over time. The learning architecture uses data from an array of three inertial measurement units attached to the lower limbs of individuals. The validation of this work is performed by the recognition of level-ground walking, ramp ascent and ramp descent, and the prediction of gait periods. The recognition of walking activity and gait period is 100% and 98.63%, respectively, when the CNN model is employed alone. The recognition of gait periods achieves a 99.9% accuracy, when the PIG method and adaptive combination are also used. These results demonstrate the benefit of having a system capable of predicting or anticipating the next information or event over time. Overall, the learning architecture offers an alternative approach for accurate activity recognition, which is essential for the development of wearable robots capable of reliably and safely assisting humans in activities of daily living.

Quantifying Mobility Scooter Performance in Winter Environments

ObjectivesTo quantify mobility scooter performance when traversing snow, ice, and concrete in cold temperatures and to explore possible performance improvements with scooter winter tires. DesignCross-sectional. SettingHospital-based research institute. ParticipantsTwo drivers (50 and 100 kg) tested 8 scooter models (N=8). Two mobility scooters were used for winter tire testing. InterventionsScooters were tested on 3 different conditions in a random sequence (concrete, 2.5-cm depth snow, bare ice). Ramp ascent and descent, as well as right-angle cornering up to a maximum of 10° slopes on winter conditions, were observed. Winter tire testing used the same slopes with 2 scooters on bare and melting ice surfaces. Main Outcome MeasuresMaximum achievable angle (MAA) and tire traction loss for ramp ascent and descent performance. The ability to steer around a corner on the ramp. ResultsAll scooters underperformed in winter conditions, specifically when traversing snow- and ice-covered slopes (χ2 [2, N=8]=13.87-15.55, P<.001) and corners (χ2 [2, N=8]=12.25, P<.01). Half of the scooters we tested were unable to climb a 1:12 grade (4.8°) snow-covered slope without losing traction. All but 1 failed to ascend an ice-covered 1:12 grade (4.8°) slope. Performance was even more unsatisfactory for the forward downslopes on both snow and ice. Winter tires enhanced the MAA, permitting 1:12 (4.8°) slope ascent on ice. ConclusionsMobility scooters need to be designed with winter months in mind. Our findings showed that Americans with Disabilities Act–compliant built environments, such as curb ramps that conform to a 1:12 (4.8°) slope, become treacherous or impassible to mobility scooter users when covered in ice or snow. Scooter manufacturers should consider providing winter tires as optional accessories in regions that experience ice and snow accumulation. Additional testing/standards need to be established to evaluate winter mobility scooter performance further.

FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

PurposeThe purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent.Design/methodology/approachGait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis.FindingsThe effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains.Practical implicationsThis study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains.Originality/valueThe noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

A four-bar knee joint measurement walking system for prosthesis design.

Gait analysis is important for the lower limb prosthesis design. Simulating the natural motion of the human knee in different terrains is useful for the design and performance assessment of the prosthetic knee. This study aimed to propose a four-bar knee joint measurement system which can simulate the natural knee motions to collect the kinetic parameters precisely and analyze the walking characteristics under different terrain conditions. A low-cost four-bar knee joint mechanism was proposed and gait characteristics were assessed on level ground, ascending and descending stairs, and ascending and descending ramp. The initial knee flexion angle during stair ascent at heel strike is obviously larger than in other walking scenes. The stance phase accounts for 53% of a single gait cycle during stair descent, which is slightly lower than other walking scenarios. The period that both the hindfoot and forefoot contact the ground in ramp descent accounts for 18%, which is less than for the others. While the forefoot contacts the ground in ramp ascent, the maximum vertical ground reaction force of the forefoot occurs when the hindfoot and forefoot simultaneously contact the ground, whereas in other scenarios the forefoot contacts the ground solely. The four-bar knee joint can simulate the natural motion of the human knee accurately. The gait characteristics analysis of different walking scenarios indicated that the low-cost four-bar knee joint exoskeleton was suitable for human knee joint simulation.