This paper presents a cable-driven actuation module design that can be easily integrated into soft wearable robots to enhance their functional versatility. The actuation module is easily mountable to various wearable systems through a simple locking mechanism and customized electronics. Furthermore, the symmetrical design of the actuation module allows for multiple orientation options, significantly expanding its range of applications. Through the use of finite element analysis (FEA), we reduce the weight of the actuation module by selecting lightweight materials, without compromising its reliability and durability. The modular and lightweight characteristics of this actuator make it suited for integration into various wearable usages. We applied the modular actuator to the soft wearable lower-limb device and showed the effectiveness through metabolic cost and electromyography(EMG) signal measurements.

We previously proposed cable-driven wearable devices for exercise and gait assistance. It was a lightweight and suit-type device with programmable resistance or assistance adjustment capabilities. In this paper, we introduce 1) a wearable gym device designed to focus on lower limb muscles and 2) a stationary gym device for comprehensive strength (weight) training. The actuation module can be used interchangeably for both devices. This actuation module includes a control board and a cable-driven actuator with a smaller size, improved strength, and greater speed compared to previous version. Its compact size makes easy implementation into our proposed devices. To evaluate the effectiveness of these devices, we conducted surface electromyography (sEMG) experiments during exercises comparing the effects of the developed devices with traditional dumbbells to confirm their efficacy.

Hip assistance with cable-driven devices has been proven to help decrease the metabolic cost of gait. However, most existing devices use heavy actuating modules or provide assistance in only one direction, limiting the effectiveness. Cable-driven devices are also difficult to accurately estimate the hip position using only motor encoders, therefore utilizing various auxiliary sensors. This paper introduces a 1.5 kg cable-driven soft wearable hip assist device that can provide assistance in both flexion and extension, using a velocity-dependent delayed output feedback controller (v-DOFC). The device is designed with the consideration of ergonomics and pressure distribution of wearable parts, to increase the anchoring performance and comfort. The controller uses time-delayed feedback proportional to the velocity output state, allowing control without requiring accurate position estimation. Additionally, directional weighting is used to provide different assistance forces for extension and flexion to match different optimal assistance values. Experimental results show that the device can reduce metabolic cost by 13.8 % compared to walking without the device. The soft wearable hip assist device can be applied to help the elderly with weaker muscles to walk longer distances.

To provide an alternative to conventional large-scale fitness equipment, we previously developed a soft passive wearable device for upper limb resistance exercises that utilized elastic exercise bands. However, the user was required to manually adjust the level of strength. In this paper, we introduce a novel wearable fitness device for upper limb exercise that constitutes cable-driven actuation to control the resistance profiles. Our proposed device allows for the generation of isotonic force trajectories, similar to those produced during dumbbell lifting, utilizing custom cable-driven actuators. The cable path of the device was determined based on the results of user testing aimed at optimizing muscle stimulation levels. In addition, linear resonant actuators were installed at customized haptic handles and upper arm modules to enhance proprioceptive sensitivity and exercise efficacy. The immersive “exer-tainment” user display interface was also redesigned to increase user motivation. The efficacy of our device was assessed by comparing the surface electromyography (sEMG) activities of upper limb muscles during chest press and triceps extension exercises performed with our device versus those using traditional weight training machines and rubber bands. It was found that our device could effectively strengthen during arm exercise, such as triceps extension.

An increase in health awareness has fueled the development of fitness equipment or devices nowadays. Most conventional fitness devices have had some issues in space limitation and the high cost of equipment. With the advance in wearable robotics, we proposed a soft passive fitness wearable device for upper limb resistance exercises such as chest press, frontal raise, and chest fly. Users can customize the exercise intensity by adjusting the length of the elastic bands embedded in the wearable device. Moreover, the exer-tainment (Exercise-entertainment) user display interface was designed to motivate the user. Movements of users were estimated using inertial measurement units (IMUs), and haptic feedback was provided through the vibro-stimulation. Furthermore, the effectiveness of the proposed device was evaluated with the Borg scales representing the rating of perceived exertion (RPE) and measuring the surface electromyography (sEMG) of the three muscles located one on the shoulder and two on the chest. Both the Borg 6–20 and CR 10 scales were increased, and the normalized sEMG activities of the upper limb muscles with the activated device had more than double in magnitude compared to that with a bare condition; therefore, the proposed device has a potential effectiveness as for resistance exercise. Overall, this research devotes preliminary evidence on the benefits of the device in promoting the user to work out and contributing to the exercise effects.

We propose a simulation framework for gait assistance with ankle pathologies. We first construct the neu-romuscular walking model, then design the parameters for assistance torques for stance and swing legs. The parameter values are determined by performing dynamic optimizations which takes into account the human-exoskeleton interactive dynamics. The simulated energy expenditure and kinematic data are compared with the real data. Case studies involve abnormal gaits with 1) foot drop, 2) foot drop and plantarflexion failure. We evaluate the gait efficiency and walking speed for the different gait types. Our result shows that each gait type should have a different assistance strategy (timing and magnitude) compared to the assistance strategy of a normal gait.

This paper presents a simulation framework for pathological gait assistance with a hip exoskeleton. Previously we had developed an event-driven controller for gait assistance [1]. We now simulate (or optimize) the gait assistance in ankle pathologies (e.g., weak dorsiflexion or plantarflexion). It is done by 1) utilizing the neuromuscular walking model, 2) parameterizing assistive torques for swing and stance legs, and 3) performing dynamic optimizations that takes into account the human-robot interactive dynamics. We evaluate the energy expenditures and walking parameters for the different gait types. Results show that each gait type should have a different assistance strategy comparing with the assistance of normal gait. Although we need further studies about the pathologies, our simulation model is feasible to design the gait assistance for the ankle muscle weaknesses.

To control exoskeletons for walking gait assistance, it is of primary importance to control them to act synchronously with the gaits of users. To effectively estimate the gait cycle (or the phase within a stride) of users, we propose a new adaptive frequency oscillator (AFO). While previous AFOs successfully estimated the walking frequency from joint angles as inputs, the new AFO, called particularly-shaped adaptive oscillator (PSAO) can estimate gait cycle from the same inputs, which would have required foot contact sensors in previous approaches. To predict the effects of PSAO-based gait assistance on human walking, it has been tested with neuromuscular walking simulation. In the simulation, the gait assistance system reduced the metabolic cost of walking for some assistance patterns. The walk ratio (step length per step rate) also changed as assistance patterns shifted in phase, which is meaningful because metabolic cost of walking in general is minimal at specific walk ratio. For a prototype exoskeleton we developed, the effect of gait assistance was experimented on a human subject walking on level ground and inclining slopes to verify the predictions from the simulation: (1) physiological cost index computed from heart rate significantly decreased indicating reduction in metabolic energy expenditure; (2) walk ratio was in fact controllable to an extent.

In this paper, we present a modeling and control method for a single-port access robot developed by our robotics group at the Samsung Advanced Institute of Technology. The surgical robot consists of a snake-like 6-degree-of-freedom (DOF) guide tube, two 7-DOF tools, a 3-DOF stereo camera, and a 5-DOF slave arm. The robot is capable of reaching various surgical sites inside the abdominal cavity from a single incision on the body. To estimate the workspace and control the guide tube to a desired location, we first obtain the forward kinematics model of the guide tube and then propose a Cartesian-level controller. The wire actuation mechanism for the tools exhibit nonlinear backlash behavior because of wire compliance and friction between the wire and Teflon-coated conduit. We compensate for the backlash in the tool joints by adding the backlash inverse with smoothing term as a feedforward term.

This paper addresses a vision-based 3D motion estimation framework for humanoid robots, which copes with human-like walking pattern. A humanoid robot, called Roboray, is designed for dynamic walking control with heel-toe motion like a human. In spite of stability and energy efficiency of the dynamic walking, it accompanies larger swaying motion and more uncertainty in camera movement than the conventional ZMP (Zero Moment Point)-based walking does. The framework effectively uses on-board odometry information from the robot to improve the performance of the visionbased motion estimation. To accomplish this, we propose an onboard odometry filter which fuses kinematic odometry, visual odometry, and raw IMU data. And the odometry filter is combined with vision-based SLAM to provide accurate motion model, so it enhances the SLAM estimates. Experimental results in indoor environment verify that the framework can successfully estimate the pose of Roboray in real-time.

The robust perception of robots is strongly needed to handle various objects skillfully. In this paper, we propose a novel approach to recognize objects and estimate their 6-DOF pose using 3D feature descriptors, called Geometric and Photometric Local Feature (GPLF). The proposed descriptors use both the geometric and photometric information of 3D point clouds from RGB-D camera and integrate those information into efficient descriptors. GPLF shows robust discriminative performance regardless of characteristics such as shapes or appearances of objects in cluttered scenes. The experimental results show how well the proposed approach classifies and identify objects. The performance of pose estimation is robust and stable enough for the robot to manipulate objects. We also compare the proposed approach with previous approaches that use partial information of objects with a representative large-scale RGB-D object dataset.