Functional Electrical Stimulation (FES), a key therapy for improving extremity function (e.g., in post-stroke patients), is limited by rapid FES-induced muscle fatigue. Additionally, Electromyography (EMG) monitoring is significantly compromised by FES artifacts. Mechanomyography (MMG), directly immune to such electrical FES artifacts, offers a promising alternative for fatigue estimation; however, its quantitative use for closed-loop FES remains underdeveloped. This study validated an MMG-based FES fatigue assessment system, introducing a novel wearable sensor (pressure P_MMG, microphone M_MMG) and an MMG-driven Tibialis Anterior (TA) musculotendon model with an MMG-derived fatigue index. An isometric FES fatigue protocol was conducted on control ($N=15$) and post-stroke ($N=3$) participants, recording force and MMG signals. P_MMG Mean Value (MV) signals consistently decreased with fatigue, showing strong average Pearson correlations ($\bar{r}$) with force decline in both control ($\bar{r}=0.740$) and stroke ($\bar{r}=0.928$) groups ($p \leq 0.005$). Conversely, M_MMG signals exhibited inconsistent trends and weaker force correlations, largely due to non-monotonic behavior in many participants. The P_MMG MV-driven model accurately predicted force decline, achieving mean coefficients of determination ($R^{2}$) of 0.741 (control) and 0.774 (stroke), with strong prediction correlations ($\bar{r} > 0.87, p < 0.01$). Model predictions utilizing M_MMG signals were successful only for participant subsets with consistent signal trends. The pressure-based P_MMG sensor provided a robust, non-invasive FES-induced fatigue indicator. The P_MMG-driven model allows continuous estimation of force capacity decline, promising for closed-loop FES to optimize rehabilitation.

In hand gesture recognition, classifying gestures across multiple arm postures is challenging due to the dynamic nature of muscle fibers and the need to capture muscle activity through electrical connections with the skin. This paper presents a gesture recognition architecture addressing the arm posture challenges using an unsupervised domain adaptation technique and a wearable mechanomyogram (MMG) device that does not require electrical contact with the skin. To deal with the transient characteristics of muscle activities caused by changing arm posture, Continuous Wavelet Transform (CWT) combined with Domain-Adversarial Convolutional Neural Networks (DACNN) were used to extract MMG features and classify hand gestures. DACNN was compared with supervised trained classifiers and shown to achieve consistent improvement in classification accuracies over multiple arm postures. With less than 5 minutes of setup time to record 20 examples per gesture in each arm posture, the developed method achieved an average prediction accuracy of $87.43 \%$ for classifying 5 hand gestures in the same arm posture and $64.29 \%$ across 10 different arm postures. When further expanding the MMG segmentation window from $200 \,\mathrm{ms}$ to $600 \,\mathrm{ms}$ to extract greater discriminatory information at the expense of longer response time, the intra-posture and inter-posture accuracies increased to $92.32 \%$ and $71.75 \%$. The findings demonstrate the capability of the proposed method to improve generalization throughout dynamic changes caused by arm postures during non-laboratory usages and the potential of MMG to be an alternative sensor with comparable performance to the widely used electromyogram (EMG) gesture recognition systems.

The development of powered lower-limb pros-theses and exoskeletons (LLPE) for assisting individuals in activities of daily living has been gaining increasing interest in the robotic community. To assist wearers walking on various terrains in daily environments, accurate gait-mode recognition and seamless transition of control strategies are crucial for these devices. Due to the high diversity of terrains, such capabilities are usually subject to terrain conditions, making terrain detection an essential issue for LLPE control. In the paper, we proposed a method for terrain detection and modeling aimed at reconstructing terrains rather than merely classifying them to provide richer information for LLPE control, including online 2D terrain information and the relative foot position. The implementation of the proposed method relies on a sensor group consisting of a low-cost single-point laser sensor and two inertial measurement units (IMU) to simultaneously and continuously capture lower limb kinematic features and terrain features. A time-varying Kalman filter is employed to fuse these features, facilitating rapid and accurate modeling of different terrains such as level ground, stairs ascend/descend, and ramp ascend/descend. The performance of the proposed method was evaluated via experiments with two healthy subjects. The results show that the reconstructed terrains can provide accurate information for LLPE control, demonstrating the effectiveness and adaptability of the proposed method.

To control wearable robotic systems, it is critical to obtain a prediction of the user's motion intent with high accuracy. Surface electromyography (sEMG) recordings have often been used as inputs for these devices, however bipolar sEMG electrodes are highly sensitive to their location. Positional shifts of electrodes after training gait prediction models can therefore result in severe performance degradation. This study uses high-density sEMG (HD-sEMG) electrodes to simulate various bipolar electrode signals from four leg muscles during steady-state walking. The bipolar signals were ranked based on the consistency of the corresponding sEMG envelope's activity and timing across gait cycles. The locations were then compared by evaluating the performance of an offline temporal convolutional network (TCN) that mapped sEMG signals to knee angles. The results showed that electrode locations with consistent sEMG envelopes resulted in greater prediction accuracy compared to hand-aligned placements (p$< $0.01). However, performance gains through this process were limited, and did not resolve the position shift issue. Instead of training a model for a single location, we showed that randomly sampling bipolar combinations across the HD-sEMG grid during training mitigated this effect. Models trained with this method generalized over all positions, and achieved 70% less prediction error than location specific models over the entire area of the grid. Therefore, the use of HD-sEMG grids to build training datasets could enable the development of models robust to spatial variations, and reduce the impact of muscle-specific electrode placement on accuracy.

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement.

As a life-threatening disease, stroke can lead to long-term problems affecting the patients’ daily living ability. A common problem facing post-stroke patients is foot drop. An emerging modality of interest for correcting the foot drop is to combine both actuated ankle-foot orthosis (AAFO) and functional electrical stimulation (FES). Such hybrid assistive system not only ensure effective assistance but also can avoid fast muscular fatigue due to excessive muscular stimulation. Due to the significant changes in the ankle joint’s kinematics and kinetics with gait cycles, optimization control strategies for hybrid AAFO and FES systems are highly demanded. However, it is challenging to develop accurate gait phase detection algorithms to guide the control of AAFO and FES while ensuring robustness with respect to the diversity and variability of patients’ gaits. In this paper, we present a novel swing sub-phase detection algorithm based on a moving average convergence divergence (MACD) indicator. The proposed detection algorithm uses only information collected from the affected leg by means of two inertia measurement units (IMU) and the AAFO. Moreover, a gait-phase based control strategy is developed to optimize the assistive effect of a hybrid AAFO and FES system. Experimental results with five healthy show the potential of the proposed approaches in ensuring both satisfactory ankle joint trajectory tracking and effective reduction in stimulation intensity, compared to the use of conventional FES assistance.

In this article, a proxy-based force control method is proposed for three important human-robot interaction modes: zero-impedance mode, force assistive mode, and large force mode. A two-mass dynamic-model-based nonlinear disturbance observer is used to meet the zero-impedance output and accurate force tracking requirements with respect to disturbances from the wearer and environment. Additionally, significant force compliance can be achieved to guarantee the wearer's safety when the interaction torque is large. The proposed method is evaluated via experiments by comparison to the conventional proportional-integral-derivative and proxy-based sliding mode control methods. The results indicate that the proposed approach achieves better force tracking accuracy, robustness, and force compliance in three-mode human-robot interactions.

This paper presents a practical method to identify body segments inertial parameters of a human-exoskeleton system using affordable and easy-to-use sensors. First, the joints and the base kinematics are estimated based on the use of an extended Kalman filter and QR visual markers. Then, joints kinematics are used in a dynamic identification pipeline together with the ground reaction force and moments collected with an affordable Wii Balance Board. The identification process is done using an augmented regressor matrix to identify at once each segment mass, center of mass 3D position and inertia tensor elements of both human locomotor apparatus and exoskeleton. The proposed method is able to accurately estimate external force and moments, with less than 6 % of normalized RMS difference in average, and is experimentally validated with a subject wearing a full lower limb exoskeleton.

This paper presents an adaptive knee-joint based Functional Electrical Stimulation (FES)method to correct the foot drop of paretic patients during swing phase. The rationale behind the adaptive FES is to amplify dorsiflexor stimulation in the late swing when it is most needed in order to face the increased plantar flexor co-contraction as gastrocnemius muscles are stretched by knee re-extension. To accurately detect the swing phase (i.e., toes off (TO)and initial contact (IC)), a novel algorithm is proposed by using a foot-mounted inertial measurement unit (IMU). The proposed strategy is verified by experiments conducted with three healthy subjects and three paretic patients. The experimental results show that highly accurate detection of TO/I C can be achieved under different walking speeds and foot contact conditions (normal and abnormal gaits). The clinical experimental results with paretic patients also reveal that similar effects on ankle dorsiflexion can be observed during mid and late swing using the proposed adaptive FES with respect to the classical FES method, while the adaptive FES used lower stimulation intensity.

Gait modes, such as level walking, stair ascent/descent, and ramp ascent/descent, show different lower-limb kinematic and kinetic characteristics. Therefore, an accurate detection of these modes is critical for a wearable robot to provide appropriate power assistance. In this paper, a fast gait-mode-detection method based on a body sensor system is proposed. A fuzzy logic algorithm is used to estimate the likelihoods of gait modes in real time. Since the proposed fast gait mode detection makes it possible to select appropriate kinematic and kinetic models for each gait mode, assistive torques required for assisting the human motions can be obtained more naturally and immediately. The proposed methods are all verified by experiments with a lower-limb exoskeletal assistive robot with transparent actuation by series elastic actuators, called the exoskeletal robotic orthosis for walking assistance. Four healthy subjects participated in the experiments. All subjects were asked to perform different gait modes using their normal and simulated abnormal gaits, i.e., blocking the knee joint of one leg during walking. Latency and success rate of gait mode detection are selected as performance criteria. The effectiveness of the proposed gait-mode-based assistive strategy is evaluated using electromyography muscular activities.

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. To provide appropriate power assistance for the sit-to-stand movement, a novel intention-based Active Impedance Control (AIC) strategy applied on a lower limb exoskeleton is proposed in this paper. The AIC is able to adapt the mechanical impedance of the human-exoskeleton system towards a desired one using the exoskeleton's power assistance. In the AIC structure, a human joint torque observer is designed to estimate the human joint torques using joint angles information instead of electromyography (EMG) or force/torque sensors; a time-varying desired impedance model is proposed according the wearer's lower limb motion ability. Simulations were implemented to illustrate the characteristics and performances of the proposed approach. Experiments with a healthy subject were carried out to evaluate the effectiveness of the proposed method. The experiments show satisfactory results in terms of appropriate power assist based on the wearer's motion intention.

Recognition of the wearer's motion intention plays an important role in the study of power-assist robots. In this paper, an intention-guided control strategy is proposed and applied to an upper-limb power-assist exoskeleton. Meanwhile, a human-robot interface comprised of force-sensing resistors (FSRs) is designed to estimate the motion intention of the wearer's upper limb in real time. Moreover, a new concept called the “intentional reaching direction (IRD)” is proposed to quantitatively describe this intention. Both the state model and the observation model of IRD are obtained by studying the upper limb behavior modes and analyzing the relationship between the measured force signals and the motion intention. Based on these two models, the IRD can be inferred online using an adapted filtering technique. Guided by the inferred IRD, an admittance control strategy is deployed to control the motions of three DC motors placed at the corresponding joints of the robotic arm. The effectiveness of the proposed approaches is finally confirmed by experiments on a 3 degree-of-freedom (DOF) upper-limb robotic exoskeleton.

Recognizing the user motion intention plays an important role in the study of power-assist robots. An intention-guided control strategy is proposed for the upper-limb power-assist exoskeleton. A force sensor system comprised of force sensing resistors (FSRs) is designed to online estimate the motion intention of user upper limb. A new concept called "intentional reaching direction (IRD)" is proposed to quantitatively describe this intention. Both the state model and the observation model of IRD are obtained by enumerating the upper limb behavior modes and analyzing the relationship between the measured force signals and the motion intention. Based on these two models, the IRD can be online inferred by applying filtering technology. Guided by the estimated IRD, an admittance control strategy is assumed to control the motions of three DC motors in the joints of the robotic arm. The effectiveness of the proposed approaches is finally confirmed by the experiments on a 3-DOF robotic exoskeleton.