This paper presents an insole-type sensor module with a novel leaf-inspired force-sensitive resistor (FSR) array for accurate three-dimensional ground reaction force (GRF) estimation during human’s various motions. Joint torque analysis, essential for numerous applications in biomechanics and wearable robotics, necessitates the measurement of three-dimensional GRF vector information, traditionally achieved in indoor environments using costly force plates. To overcome these limitations, this study proposes an alternative method by incorporating FSRs on three inclined planes within the insole. A vector scaling process transforms the force values from the FSRs into the three-dimensional force vector, enabling continuous and user-independent estimation of GRF. The sensor module is integrated with machine learning, demonstrating its accuracy and usability in various motion scenarios. The results confirm the effectiveness of the leaf-inspired FSR array, giving the possibilities for portable and cost-effective motion analysis systems.

Traditionally, powered exoskeletons have predominantly featured a back-enveloping design due to its simplicity in both implementation and user donning. However, this design results in a backward shift of the center of mass (CoM) in the sagittal plane. This paper identifies the limitations of existing design approaches and determines the optimal anterior-posterior (A/P) CoM position considering factors like actuating power, balance in the neutral posture, and user’s hand workspace. Our optimization analysis recommends placing the CoM in front of the user. We address historical constraints on front-enveloping designs and propose solutions. Furthermore, we validate the usability of our designed exoskeleton through testing with a complete paraplegic user.

Hand gesture recognition has received considerable attention as an intuitive interaction method in recent years. This research introduces a new wearable hand gesture recognition system that employs pneumatic mechanomyography (pMMG) to directly monitor the wrist tendon group, which transmits muscle force to the fingers. The experimental findings demonstrate that the proposed method provides raw observations proportional to the finger flexion force, with highR-squared values exceeding 0.94. The performance of the proposed system was evaluated by conducting a hand gesture experiment consisting of 28 hand gestures. The proposed method achieved an average accuracy of 98.12%, surpassing the surface electromyography (sEMG) system's accuracy of 93.89%. Furthermore, the fusion of pMMG and sEMG sensors yielded an accuracy of 99.18%. The results suggest that the proposed approach exhibits the potential to enhance the accuracy and efficiency of hand gesture recognition systems.

Accurate identification of gait phases is critical in effectively assessing the assistance provided by lower limb exoskeletons. In this study, we propose a novel gait phase recognition system called ObsNet to analyze the gait of individuals with spinal cord injuries (SCI). To ensure the reliable use of exoskeletons, it is essential to maintain practicality and avoid exposing the system to unnecessary risks of fatigue, inaccuracy, or incompatibility with human-centered devices. Therefore, we propose a new approach to characterize exoskeletal-assisted gait by estimating forces on exoskeletal joints during walking. Although these estimated forces are potentially useful for detecting gait phases, their nonlinearities make it challenging for existing algorithms to generalize accurately. To address this challenge, we introduce a data-driven model that simultaneously captures both feature extraction and order dependencies, and enhance its performance through a threshold-based compensational method to filter out momentary errors. We evaluated the effectiveness of ObsNet through robotic walking experiments with two practical users with complete paraplegia. Our results indicate that ObsNet outperformed state-of-the-art methods that use joint information and other recurrent networks in identifying the gait phases of individuals with SCI ($\boldsymbol{p}< \mathbf{0.05}$). We also observed reliable imitation of ground truth after compensation. Overall, our research highlights the potential of wearable technology to improve the daily lives of individuals with disabilities through accurate and stable state assessment.

Powered exoskeletons for people with complete paraplegia have been controlled based on predefined joint-reference trajectories. As the target users of such robots may not realize any voluntary movement, the human body is fully constrained and follows the movement of the powered exoskeleton joints. The predefined gait pattern, however, may or may not be adequate for every user because the gait pattern is resulting from complex interactions between the body segments and environment, as well as dynamic characteristics of the body segments. As all the persons and their body segments have different dynamic characteristics, therefore, a bespoke tuning of gait parameters is necessary in order to realize the natural gait motion, which is optimal for each user. In this article, an adaptive gait pattern adjustment method is proposed. The proposed method observes the ground contact timing, which is directly related to the adequacy of the gait pattern for the user wearing a robot. Based on the ground contact timing, the joint-reference trajectories are adjusted, which are parameterized by the trunk inclination angle. The proposed method iteratively calculates an appropriate trunk inclination angle from the information of ground contact timing. In this article, the derivation of the proposed method and its experimental verification with WalkON Suit, a powered exoskeleton, are introduced. The proposed algorithm successfully worked for two practical users with complete paraplegia, and the adapted gait patterns showed excessive performance in walking speed, oxygen consumption, palm force on crutches, etc. The results were also verified by winning both gold and bronze medals in the global competition, Cybathlon 2020, while accomplishing the best records among all the teams.

Powered exoskeletons for people with paraplegia have been widely developed. To generate the basic but essential motions for daily human life, precise control algorithms to follow the joint reference trajectories are necessary. The dynamic characteristics of the exoskeletal joints, however, varies signifi-cantly during walking because the load side is exchanged from legs in the air to the wearer’s body. To ensure robustness and tracking performance for any case of gait even in the presence of exogenous disturbances such as human’s active movements and repeated ground contacts, customized robust control algorithms need to be developed. In this paper, therefore, hybrid model control of the powered exoskeleton, WalklON Suit, utilized with the disturbance observer is introduced. A hybrid nominal model, whose model parameters are interchanged between the gait phases, i.e., swing and stance, is developed by the parameter adatpation algorithm. By the proposed method, the disturbance observer can fully reject the exogenous disturbance during walking and achieve high-performance gait assistance to the people with complete paraplegia. In this paper, the experimental verification of the designed model and the controller with the WalkON Suit, are also introduced.

Powered exoskeletons for people with paraplegia are subjected to repetitive and large impacts due to the repeated ground contacts. The repetitive impact forces not only deteriorate the wear comfort but also cause a serious damage to the muscles and bones of the human wearing the powered exoskeleton. To address this issue, a novel shock absorption mechanism for powered exoskeletons that can reduce the peak of ground reaction force up to 28% is designed in this paper. The designed absorption mechanism is integrated into the WalkON Suit, a powered exoskeleton for people with paraplegia and verified by experimental results with a human subject in this paper also.

Several powered exoskeletons have been developed and commercialized to assist people with complete spinal cord injury. For motion control of a powered exoskeleton, a normal gait pattern is often applied as a reference. However, the physical ability of paraplegics and the degrees of freedom of powered exoskeletons are totally different from those of people without disabilities. Therefore, this paper introduces a novel gait pattern depart from the normal gait, which is proper to the paraplegics. Since a human is included, the system of the powered exoskeleton has lots of motion uncertainties that may not be perfectly predicted resulting from different physical properties of paraplegics (SCI level, muscular strength of the upper body, body parameters, inertia), actions from crutches (position and timing to put), several types of training (period, methodology), etc. Then, to find a stable and safe gait pattern adapted to the individual user, an iterative way to compensate the gait pattern is also required. In this paper, human iterative learning algorithm, which utilizes the accumulated data during walking to adjust the gait trajectories is proposed. Additionally, the effectiveness of the proposed gait pattern is verified by human walking experiments.

In the development of wearable robots for complete paraplegia, human-robot interaction is one of the most important considerations. In most of those robots for complete paraplegia, users should use crutches to help balance the human-robot system. People with impaired sensory nerves of legs should be able to check their foot position to ensure contact when they walk with wearable robots. Therefore, the user interface of the wearable robot should be easy to be controlled with using crutches and be able to monitor its state. In this paper, the user interface method of the wearable robot, WalkON Suit, for complete paraplegia is introduced. The switches embedded crutches allow the user to change the operation mode without assistance. Moreover, the user can monitor operating status using the see-through display on glasses. The experimental results of walking trials are also introduced.

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.

A Spring-Loaded-Inverted Pendulum (SLIP) model has been applied to many legged robots, such as quadruped robots, for realizing trotting, bounding, and galloping motions. The indecipherable damping factors, however, hindered the implementation of the SLIP model in practice. In this paper, a control algorithm is proposed to realize the ideal springy motion of a robotic leg. A Kalman filter with a damped SLIP model as the reference system is utilized for estimating a longitudinal velocity of the robotic leg (i.e., the length change rate between the proximal joint and the tip toe). By cancelling the undesired damping factors through positive feedback based on the Kalman filter estimate, the robotic leg is controlled to realize an undamped SLIP model. The proposed method is verified by simulation and experiment. The results showed that the proposed control algorithm enabled the robotic leg to keep continuously hopping as a spring even in the presence of nonlinear frictions.

In applications of human-robot interactions, accurate measurement of interactive forces between the human and the robot plays a significant role. Such sensors should not only be accurate and reliable, but also be soft enough to guarantee the safe and compliant human-robot interaction. In this aspect, pneumatic sensors with soft air chambers have often been utilized as a soft force measurement system. Although such a sensor system provides a good compliance and softness, however, it measures only a lumped force acting on the chamber, because the measurement of the pneumatic sensor is the pressure change in the air chamber. For the measurement of multi-dimensional interaction forces with high softness and compliance, a three-axis force measurement system is devised by arranging three air chambers in a radially symmetric pattern. Each air chamber embeds a pneumatic sensor, and the pressure changes in the three air chambers are measured in order to distinguish the direction of the applied force. By decoupling the sensor signals from the three pneumatic sensors, the three-dimensional force components can be calculated accurately. Consequently, the proposed sensor system is able to measure the three-axis forces while maintaining the great softness and compliance provided by the soft air chambers. The design, the fabrication method, and the verification of the proposed method are introduced in this paper.

Actuators for human-interactive robot systems require transparency and guaranteed safety. An actuation system is called transparent, when it is able to generate an actuation force as desired without any actuator dynamics. The requirements for the transparent actuation include high precision and large frequency bandwidth in actuation force generation, zero mechanical impedance, and so on. In this paper, a compact rotary series elastic actuator (cRSEA) is designed considering the actuation transparency and the mechanical safety; the mechanical parameters of a cRSEA are optimally selected for the controllability, the input and output torque transmissibility, and the mechanical impedance. A mechanical clutch that automatically disengages the transmission is devised such that the human is mechanically protected from an excessive actuation torque due to any possible controller malfunction or any external impact from a collision. The proposed cRSEA with a mechanical clutch is applied to develop a wearable robot for incomplete paraplegic patients. Experimental results of a manufactured cRSEA system are introduced in this paper also.

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.

This paper introduces a state estimation method for the position control of a two-DoF planar manipulator in the polar coordinate system. The kinematics and dynamics of the two-DoF manipulator are analyzed utilizing the biarticular actuation mechanism and the polar coordinate system, which makes it possible to express the equation of motion in an effective and convenient way. The dynamic equation with the proposed coordinate system and biarticular actuation mechanism is formalized into a state-space equation, and the state feedback controller is designed based on the derived state-space dynamics. As applications of the proposed method, a position tracking controller is designed at the end-effector. In addition, for implementation of the proposed method, a kinematic Kalman filtering is utilized for the best estimation of state variables.

A series elastic actuator (SEA) is a promising actuation method in robotics and mechatronics applications that intelligently interact with environments, including humans. The SEA is characterized by a spring placed between the load and an actuator, which is an electric motor in most SEA systems. Since the spring plays the role of a transducer between a position (i.e., spring deflection) and a force, it is able to control the output force (torque) precisely by utilizing typical position control methods. Also, realization of the precise force (torque) control implies that the SEA exhibits zero mechanical impedance, which renders its superior advantage in human-interactive systems. In many applications, however, the dynamic characteristics of an SEA have not been considered in controller design process, and mostly PID (Proportional-Integral-Derivative) control has been applied to as controllers for SEA. However, PID controller has various drawbacks including wind-ups and thus there are rooms to be improved for SEA control. This paper, taking into considerations this, proposes a dynamic model based control design of SEA. The dynamic characteristics of SEA are analyzed theoretically and experimentally, and feedback controllers based on the derived dynamics is proposed. Comparisons with conventional control methods verify the advantage of the proposed dynamic model based controller on control performance and force observation.

Recognition of human gestures plays an important role in a number of human-interactive applications, such as mobile phones, health monitoring systems, and human-assistive robots. Electromyography (EMG) is one of the most common and intuitive methods used for detecting gestures based on muscle activities. The EMG, however, is in general, too sensitive to environmental disturbances, such as electrical noise, electromagnetic signals, humidity, and so on. In this paper, a new method for recognizing the muscular activities is proposed based on air-pressure sensors and air-bladders. The muscular activity is detected by measuring the change of the air pressure in an air-bladder contacting the interested muscle(s). Since the change of the air pressure can be more robustly measured compared with the change of electric signals appeared on the skin, the proposed sensing method is useful for mobile devices due to its great signal-to-noise ratio (SNR) and fast response time. The principle and applications of the proposed sensing method are introduced in this paper. The performance of the proposed method is evaluated in terms of linearity, repeatability, wear-comfort, etc., and is also verified by comparing it with an EMG signal and a motion sensor.

A number of control methodologies have been studied for assistive robotic technologies. Since the human motions in a daily life consist of multiple phases, such as walking, sitting and standing, controllers for assistive robots are required to be able to cope with different motion phases. For this reason, hybrid control which is able to occasionally switch control algorithms according to the motion phases has been preferred in the assistive robots, in particular wearable robots. In this paper, a generalized control framework is proposed as a fundamental framework for the hybrid assistive control and its stability is analyzed using the framework. The proposed control framework is implemented into a lower-limb exoskeleton robot and its effectiveness is verified thorough experiments.

In this paper, kinematics, statics, and dynamics of a two-link manipulator with a biarticular actuation mechanism are discussed. The biarticular actuation mechanism is inspired from the musculoskeletal structure of animals and is utilized in the controller design, as well as the mechanism design. For an effective and convenient expression of the equation of motion, the rotating coordinate system is adopted unlike the conventional robotic manipulators, the dynamics of which are obtained in the fixed coordinate system. It is proved in this paper that the biarticular actuation mechanism makes the control of the end-effector easier, more robust, and more intuitive than typical actuation mechanisms. Based on the derived equation of motion of a robotic manipulator in the rotating coordinate system, a disturbance-observer-based controller is proposed for realization of the Spring Loaded Inverted Pendulum (SLIP) model, which is a common model of human lower extremities but has seldom been realized in practice. The proposed methods are all verified by simulation studies in this paper.

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

Quadruped robots are regarded as a new trend in robotics due to their superior gait stability and robustness to disturbances. More recently, many robotics researchers are making their best efforts to improve the locomotion speed, as well as the stability and robustness, of quadruped robots. The high-speed locomotion creates various challenges in the development of actuators, mechanical design, and control algorithms of the robot. In this paper, a linear actuation system for the high-speed locomotion of a quadruped robot is introduced. The proposed actuator is designed based on the principle of brushed direct-current electric motor systems. For the minimal impedance and improved force capacity, the actuator is designed with dual layers of cores, which are aligned parallel to permanent magnets. The mechanical and electrical properties of the actuation system, such as back-drivability, controllability, and response time, are verified by experimental results. A robotic leg, which is the rear leg of a cheetah-like robot, is designed with the proposed actuator, and is introduced briefly in this paper also.

Complementary filtering (CF) is a well known method that can effectively fuse a gyroscope and accelerometer measurement in order to robustly estimate the attitude of a rigid body in a planar single degree of freedom (DOF) setting. The attitude can be estimated individually by either integrating the gyroscope measurement or by calculating the inverse tangent of the components of a 2-axis accelerometer. The gyroscope can adequately estimate the angle in the higher frequency region, but suffers from drift issues at low frequency, whereas the accelerometer can accurately measure the acceleration and thus direction of gravity, but loses this accuracy when faced with motion accelerations. CF traditionally uses linear time invariant filters, however, this paper presents an extension to the CF method by proposing time-varying parameters. A fuzzy logic method is developed to adjust the parameters. Stability analysis as well as experimental results are presented to verify the proposed method.

Precise and large torque generation, back-drivability, low output impedance, and compactness of hardware are important requirements for human assistive robots. In this paper, a compact rotary series elastic actuator (cRSEA) is designed considering these requirements. To magnify the torque generated by an electric motor in the limited space of the compact device, a worm gear is utilized. However, the actual torque amplification ratio provided by the worm gear is different from the nominal speed reduction ratio due to friction, which makes the controller design challenging. In this paper, the friction effect is considered in the model of cRSEA, and a robust control algorithm is designed to precisely control the torque output in the presence of nonlinearities such as the friction. The mechanical design and dynamic model of the proposed device and the design of a robust control algorithm are discussed, and actuation performance is verified by experiments.

Realizing an ideal impedance control system in lower extremity rehabilitation systems is challenged by mechanical impedance of robot hardware. Although some studies in the field of control systems have been helpful in reducing the mechanical impedance of actuators, they have not been able to remove the inertia of robot hardware. This paper introduces an alternative design in which mechanical links are utilized. The mechanical links are driven by one actuator without any complicated servo systems. The design parameters are optimized for the link system to generate the normal walking motion. The simulation data shows that the normal gait patterns are realized successfully. The device is connected to a patient using elastic components, and therefore the inertia of the robot is not directly imposed on the patient. The patient's legs are guided to follow the motion of the robot with the forces generated by the elastic components.

Exoskeletons are attracting a great attention as a new means of rehabilitation devices. In such applications, control algorithms of exoskeletons are often inspired by nature for natural and effective assistance for patients. In this paper, a control algorithm is inspired by aquatic therapy. Aquatic therapy has various benefits for rehabilitation processes based on useful properties of water, e.g. buoyancy and drag. However, realization of such effects is challenged by limitations in hardware, such as mechanical impedance or impreciseness of actuator forces. Therefore, the resistive forces generated by actuators, which cause serious discomfort to patients, are precisely modeled and compensated to realize the control algorithm inspired by aquatic therapy effectively. The proposed methods are implemented in SUBAR developed by Sogang University and verified by experiments.

The Sogang University biomedical assistive robot (SUBAR), which is an advanced version of the exoskeleton for patients and the old by Songang (EXPOS) is a wearable robot developed to assist physically impaired people. It provides a person with assistive forces controlled by human intentions. If a standard geared DC motor is applied, however, the control efforts will be used mainly to overcome the resistive forces caused by the friction, the damping, and the inertia in actuators. In this paper, such undesired properties are rejected by applying a flexible transmission. With the proposed method, it is intended that an actuator exhibits zero impedance without friction while generating the desired torques precisely. Since the actuation system of SUBAR has a large model variation due to human-robot interaction, a control algorithm for the flexible transmission is designed based on a robust control method. In this paper, the mechanical design of SUBAR, including the flexible transmission and its associated control algorithm, are presented. They are also verified by experiments.

Measurement of ground contact forces (GCF) provides necessary information to detect human gait phases. In this paper, a new analysis method of the GCF signals is discussed for detection of the gait phases. Human gaits are complicated, and the gait phases can not be exactly distinguished by comparing sensor outputs to a threshold. This paper mainly discusses how to detect the gait phases continuously and smoothly. The proposed analysis method is intended for applications to power assistive devices for patients, as well as diagnostics of pathological gait. Smooth and continuous detection of the gait phases enables a full use of information obtained from GCF sensors. For experimental verification, smart shoes have been developed. Each smart shoe has four GCF sensors embedded between the cushion pad and the sole. The performances are experimentally verified for both normal and abnormal gaits, and a means for quantification of abnormalities in the gait is also introduced in this paper.

Rehabilitation devices require a very precise actuating system. In this paper, a flexible joint actuator is proposed as an actuating system of an intelligent active orthosis. To generate joint torque as desired, a spring is installed between a motor and human joint and the motor is controlled to have a proper spring deflection for torque control. When the desired torque is zero, the motor should follow human joint motion which requires that the friction and inertia of the motor are compensated. The human joint and body part represent the load to the flexible joint actuator. They interact with environment and their parameters are not fixed. The controller for the flexible joint actuator must operate under these conditions.

Recently the technology of capsule endoscopy has developed dramatically and many researchers are making efforts to combine surgical function into the capsule type endoscope. In this paper, the micro biopsy module which is a part of the capsule endoscope is proposed. The proposed module is less than 2 mm in thickness and has a diameter of 10 mm. It consists of a trigger with paraffin block, rotational tissue-cutting razor with a torsion spring and controller. This module makes it possible for the capsule endoscope to obtain a sample tissue inside the small intestine which can not be reached by a conventional biopsy device. Through dedicated experiments, tissue samples were successfully extracted using the proposed biopsy module and the cells in samples were extracted and tested by a microscope.