Robotic Glove Research Articles

Somesthetic, Visual, and Auditory Feedback and Their Interactions Applied to Upper Limb Neurorehabilitation Technology: A Narrative Review to Facilitate Contextualization of Knowledge.

Reduced hand dexterity is a common component of sensorimotor impairments for individuals after stroke. To improve hand function, innovative rehabilitation interventions are constantly developed and tested. In this context, technology-based interventions for hand rehabilitation have been emerging rapidly. This paper offers an overview of basic knowledge on post lesion plasticity and sensorimotor integration processes in the context of augmented feedback and new rehabilitation technologies, in particular virtual reality and soft robotic gloves. We also discuss some factors to consider related to the incorporation of augmented feedback in the development of technology-based interventions in rehabilitation. This includes factors related to feedback delivery parameter design, task complexity and heterogeneity of sensory deficits in individuals affected by a stroke. In spite of the current limitations in our understanding of the mechanisms involved when using new rehabilitation technologies, the multimodal augmented feedback approach appears promising and may provide meaningful ways to optimize recovery after stroke. Moving forward, we argue that comparative studies allowing stratification of the augmented feedback delivery parameters based upon different biomarkers, lesion characteristics or impairments should be advocated (e.g., injured hemisphere, lesion location, lesion volume, sensorimotor impairments). Ultimately, we envision that treatment design should combine augmented feedback of multiple modalities, carefully adapted to the specific condition of the individuals affected by a stroke and that evolves along with recovery. This would better align with the new trend in stroke rehabilitation which challenges the popular idea of the existence of an ultimate good-for-all intervention.

Personalized voice activated grasping system for a robotic exoskeleton glove

This paper proposes a novel human machine interface (HMI) and electronics system design to control a rehabilitation robotic exoskeleton glove. Such system can be activated with the user’s voice, take voice commands as input, recognize the command and perform biometric authentication in real-time with limited computing power, and execute the command on the exoskeleton. The electronics design is a stand-alone plug-and-play modulated design independent of the exoskeleton design. This personalized voice activated grasping system achieves better wearability, lower latency, and improved security than any existing exoskeleton glove control system.

Probabilistic Model-Based Learning Control of a Soft Pneumatic Glove for Hand Rehabilitation.

Stroke survivors are usually unable to perform activities of daily living (ADL) independently due to loss of hand functions. Soft pneumatic gloves provide a promising assistance approach for stroke survivors to conduct ADL tasks. However, few studies have explored effective control strategies for the 'human-soft robot' integrated system due to challenges in the nonlinearities of soft robots and uncertainties of human intentions. Therefore, this work aims to develop control approaches for the system to improve stroke survivors' hand functions. Firstly, a soft pneumatic glove was utilized to aid with stroke-impaired hands. Secondly, a probabilistic model-based learning control approach was proposed to overcome the challenges. Then a task-oriented intention-driven training modality was designed. Finally, the control performance was evaluated on three able-bodied subjects and three stroke survivors who attended 20-session rehabilitation training. The proposed approach could enable the soft pneumatic glove to provide adaptive assistance for all participants to accomplish different tasks. The tracking error and muscle co-contraction index showed decreasing trends while the hand gesture index showed an increasing tendency over training sessions. All stroke survivors showed improved hand functions and better muscle coordinations after training. This work developed a learning-based soft robotic glove training system and demonstrated its potential in post-stroke hand rehabilitation. This work promotes the application of soft robotic training systems in stroke rehabilitation.

Increasing motor cortex activation during grasping via novel robotic mirror hand therapy: a pilot fNIRS study

BackgroundMirror therapy (MT) has been used for functional recovery of the affected hand by providing the mirrored image of the unaffected hand movement, which induces neural activation of the cortical hemisphere contralateral to the affected hand. Recently, many wearable robots assisting the movement of the hand have been developed, and several studies have proposed robotic mirror therapy (RMT) that uses a robot to provide mirrored movements of the unaffected hand to the affected hand with the robot controlled by measuring electromyography or posture of the unaffected hand. In some cases of RMT a mirror is placed to allow the person to observe only the unaffected hand but in others users simply observe the robotically assisted hand performing the mirrored movements, as was the case in this study. There have been limited evaluations of the cortical activity during RMT compared to MT and robotic therapy (RT) providing passive movements despite the difference in the modality of sensory feedback and the involvement of motor intention, respectively.MethodsThis paper analyzes bilateral motor cortex activation in nine healthy subjects and five chronic stroke survivors during a pinching task performed in MT, RT, and RMT conditions using functional near infrared spectroscopy (fNIRS). In the MT condition, the person moved the unaffected hand and observed it in a mirror while the affected hand remained still. In RT condition passive movements were provided to the affected hand with a cable-driven soft robotic glove, while, in RMT condition, the posture of the unaffected hand was measured by a sensing glove and the soft robotic glove mirrored its movement on the affected hand.ResultsFor both groups, the RMT condition showed the greatest mean cortical activation on the motor cortex contralateral to the affected (non-dominant for the healthy group) hand compared to other conditions. Individual results indicate that RMT induces similar or greater neural activation on the motor cortex compared to MT and RT conditions. The interhemispheric activations of both groups were balanced in RMT condition. In MT condition, significantly greater activation was shown on the hemisphere ipsilateral to the affected (dominant for the healthy group) hand for both subject groups, while the contralateral side showed significantly greater activation for the healthy group in RT condition.ConclusionThe experimental results indicate that combining visual feedback, somatosensory feedback, and motor intention are important for greater stimulation on the contralateral motor cortex of the affected hand. RMT that includes these factors is hypothesized to achieve a more effective functional rehabilitation due to greater and more balanced cortical activation.

SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.

Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05 ± 8.03, p = 0.001), FMA shoulder/elbow (6.2 ± 5.94, p = 0.0004) and FMA wrist/hand (4.3 ± 2.83, p = 0.007), and WMFT (5.1 ± 5.53, p = 0.037). The improvement of FMA was significantly correlated with BCI accuracy (r = 0.714, p = 0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.

Neuromuscular electrical stimulation restores upper limb sensory-motor functions and body representations in chronic stroke survivors

A conventional treatment outcome is suboptimal for sensory impairments in stroke patients. Novel approaches based on electrical stimulation or robotics are proposed as an adjuvant for rehabilitation, though their efficacy for motor, sensory, and body representation recovery have not been tested. Sixty chronic stroke patients with unilateral motor deficits were included in a pseudo-randomized open-label multi-arm control trial (ClinicalTrials.gov: NCT03349138). We tested the effects of a robotic glove (GloReha [GR]) and a new neuromuscular electrical stimulation system (Helping Hand [HH]) and compared them with conventional treatment (CT) in restoring motor and sensory functions and the affected limb perception. HH was designed to concurrently deliver peripheral motor activation and enhanced cutaneous sensation. Patients were split in four dose-matched groups: CT, GR, HH, and GRHH (receiving 50% GR and 50% HH). Assessments were performed at inclusion, halfway, end of treatment (week 9), and follow-up (week 13). HH provided an earlier benefit, quantified by the Motricity Index (MI), than GR. At the end of the treatment, the amelioration was higher in groups GRHH and HH and extended to somatosensory functions. These benefits persisted at the follow-up. GRHH and HH also improved the perceived dimensions and altered feeling toward the affected limb. Interestingly, the reduction of altered feelings correlated with MI improvements and depended on the amount of HH. We suggest that HH concurrently stimulates sensory and motor systems by generating an enhanced cutaneous sensation, coherent in location with the elicited motor recruitment, leading to ameliorated sensorimotor functions and bodily perceptions in stroke patients. This work was supported by a Foundation advised by CARIGEST, by Fondazione CARIPLO, by the SNSF NCCR Robotics, and by the Bertarelli Foundation.

Real time control and fabrication of a soft robotic glove by two parallel sensors with MBD approach

Soft robotic gloves were designed to aid the rehabilitation process with hand pathologies and coordination of gripping exercises. The main issue in soft robotic actuators is to design a control strategy to overcome deformation in grasping exercises. In this paper, a new soft robotic actuator is developed to be protected against swell and deformation. This soft robotic glove is equipped with two sensors; these sensors make the robotic glove more intelligent. In the hardware, it was used two sensors in the new closed-loop method which include an air pressure sensor in the figure tip and a flex sensor to measure finger flexion rate. Two closed-loop control system is developed to regulate inlet air pressure and regulate the angle of the fingers for the soft robotic actuator. A Model-Based Design (MBD) method is presented as a very cost-effective, favorable, and robust method. PID programming on an embedded controller is applied by MBD approach. The soft actuator process contains a molded wooden chamber and fiber reinforcement. Experimental results show that the proposed soft robotic has a soft gripping mechanism, accurate gripping against various objects during daily activities.

Effect of proprioceptive stimulation using a soft robotic glove on motor activation and brain connectivity in stroke survivors

Objective. Soft-robotic-assisted training may improve motor function during post-stroke recovery, but the underlying physiological changes are not clearly understood. We applied a single-session of intensive proprioceptive stimulation to stroke survivors using a soft robotic glove to delineate its short-term influence on brain functional activity and connectivity. Approach. In this study, we utilized task-based and resting-state functional magnetic resonance imaging (fMRI) to characterize the changes in different brain networks following a soft robotic intervention. Nine stroke patients with hemiplegic upper limb engaged in resting-state and motor-task fMRI. The motor tasks comprised two conditions: active movement of fingers (active task) and glove-assisted active movement using a robotic glove (glove-assisted task), both with visual instruction. Each task was performed using bilateral hands simultaneously or the affected hand only. The same set of experiments was repeated following a 30 min treatment of continuous passive motion (CPM) using a robotic glove. Main results. On simultaneous bimanual movement, increased activation of supplementary motor area (SMA) and primary motor area (M1) were observed after CPM treatment compared to the pre-treatment condition, both in active and glove-assisted task. However, when performing the tasks solely using the affected hand, the phenomena of increased activity were not observed either in active or glove-assisted task. The comparison of the resting-state fMRI between before and after CPM showed the connectivity of the supramarginal gyrus and SMA was increased in the somatosensory network and salience network. Significance. This study demonstrates how passive motion exercise activates M1 and SMA in the post-stroke brain. The effective proprioceptive motor integration seen in bimanual exercise in contrast to the unilateral affected hand exercise suggests that the unaffected hemisphere might reconfigure connectivity to supplement damaged neural networks in the affected hemisphere. The somatosensory modulation rendered by the intense proprioceptive stimulation would affect the motor learning process in stroke survivors.

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

AbstractSafe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2$^\circ$and 3$^\circ$, sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1$^\circ$). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

Data Driven Calibration and Control of Compact Lightweight Series Elastic Actuators for Robotic Exoskeleton Gloves.

The working principle of a SEA is based on using an elastic material connected serially to the mechanical power source to simulate the dynamic behavior of a human muscle. Due to weight and size limitations of a wearable robotic exoskeleton, the hardware design of the SEA is limited. Compact and lightweight SEAs usually have noisy signal output, and can easily be deformed. This paper uses a compact lightweight SEA designed for exoskeleton gloves to demonstrate immeasurable strain and friction force which can cause an average of 34.31% and maximum of 44.7% difference in force measurement on such SEAs. This paper proposes two data driven machine learning methods to accurately calibrate and control SEAs. The multi-layer perception (MLP) method can reduce the average force measurement error to 10.18% and maximum error to 29.13%. The surface fitting method (SF) method can reduce the average force measurement error to 8.06% and maximum error to 35.72%. In control experiments, the weighted MLP method achieves an average of 0.21N force control difference, and the SF method achieves an average of 0.29N force control difference on the finger tips of the exoskeleton glove.

Designing Soft Pneumatic Actuators for Thumb Movements

Studies have developed various types of soft robotic gloves for hand rehabilitation in recent years. Most soft actuators achieved a sufficient thumb flexion assist while lacking opposition support, which requires the coordination of thumb flexion and abduction-adduction. The difficulties for thumb support lie in the intrinsic complexity of thumb movements and spatial restriction of the hand. To realize multiple degrees of freedom of the thumb and make effective use of the limited space of the hand's dorsal side, we optimized and compared two approaches for thumb support. The combination approach used two independent soft actuators for thumb flexion and abduction-adduction support, respectively. The all-in-one approach used one single soft actuator to assist motions in different directions. We designed the soft actuators for each approach based on the thumb's biomechanical characteristics and evaluated their thumb flexion, abduction support performance in terms of the range of motion (RoM) and force output, and the opposition support performance using an enhanced Kapandji test. The results showed a larger abduction RoM and force output of the composition approach and a higher Kapandji score of the all-in-one approach, indicating that the two approaches might be applicable for thumb support but have the advantage in different rehabilitation stages.

Learning Fingertip Force to Grasp Deformable Objects for Soft Wearable Robotic Glove With TSM

Soft wearable robotic gloves based on tendon-sheath mechanism are widely developed for assisting people with a loss of hand mobility. For these robots, knowing the fingertip forces applied to deformable objects is crucial in successfully grasping them without causing excessive deformations. Existing studies presented methods to predict fingertip force applied to rigid objects only using information from the actuation system. However, forces applied to deformable objects are subject to non-linearity and hysteresis in relation to the objects' stiffness, which further complicates the problem. Therefore, this letter proposes a deep-learning model that can accurately estimate the fingertip forces applied to deformable objects using motor encoder values, motor current, and wire tension. Our model is based on an integrated system of Long Short-Term Memory models that 1) estimates stiffness of the grasped objects and 2) incorporates the estimated stiffness for predicting the fingertip forces. When evaluated using a TSM-based soft wearable robot, the proposed model recorded fingertip force estimation of 0.702 N RMSE, achieving 45% increase in accuracy compared to LSTM that does not consider the objects' stiffness. The applicability of our method was evaluated by estimating the fingertip forces applied to common daily items and performing real-time force control.

Design of Tendon-Actuated Robotic Glove Integrated with Optical Fiber Force Myography Sensor

People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation.

A compact, compliant, and biomimetic robotic assistive glove driven by twisted string actuators

A compact, compliant, and biomimetic robotic assistive glove driven by twisted string actuators

Bioinspired High-Degrees of Freedom Soft Robotic Glove for Restoring Versatile and Comfortable Manipulation.

The human hand is one of the most complex and compact grippers that has arisen as a product of natural genetic engineering; it is highly versatile, as it handles power and precision tasks. Since proper contact points and force directions are required to ensure versatility and secure a stable grip on an object, there must be a large workspace and controllable tip force directions for the digits. Although they are important, many individuals with neuromuscular diseases experience loss of these features. Thus, we propose a high-degree-of-freedom (DOF) soft robotic glove inspired by the anatomical features of human hands. The mechanism for adjusting the position and force direction of each tip is based on the structure of the extrinsic and intrinsic muscle-tendon units. The large thumb workspace was achieved by assisting opposition/reposition and flexion/extension to enable various grasping postures. A bidirectional actuation control mechanism with a cable-actuated agonist and an elastomer antagonist increased the assisted DOF and maintained compactness. The kinematic and kinetic performances of our device were evaluated by performing tests with eight stroke survivors. The thumb workspace increased by 43%, 207%, and 248% in the distal-proximal, dorsal-palmar, and radial-ulnar directions, respectively. The pinching shear force decreased by 54% and 45% for the nonthumb digits and thumb, respectively. These device-assisted improvements allowed objects to be stably grasped and manipulated in various postures. The novel device can assist individuals with impaired hand function to improve their grasping performance. Clinical Research Information Service (CRIS) Registration Number: KCT0004855.

P-OT016. The comparison of robotic glove training and conventional training in post-stroke hand impairment patients: A pilot randomized controlled trial protocol

Introduction . Rehabilitation in stroke patients is crucial in minimizing the neurological deficit, improving their quality of life and reducing the caretakers’ burden. Post-stroke hand impairment is common and improving hand function is a core element of rehabilitation. For such patients, robotic glove training might be a promising tool. This trial aims to determine the efficacy, safety, feasibility and cost effectiveness of Robotic Glove Training (RGT) compared with Conventional Training (CT) among post-stroke unilateral hand impairment patients. Methods . This is a pilot, randomized, assessor blinded, controlled trial. Adult patients with first onset of stroke with unilateral poststroke hand impairment (Medical Research Council Grading ≤ 3) will be selected. Subjects will be randomized into RGT or CT group in a 1:1 ratio. Both groups also will undergo the usual daily home hand exercises. The endpoints will be measured after 6 weeks of training. Results . For efficacy assessment, hand grip strength; tip, key and palmar pinch strength; and hand motor function will be measured using Hand Dynamometer; Pinch Gauge; Motor Assessment Scale and Fulg Meyer Assessment. For safety assessment, hand spasticity and pain will be assessed using Modified Ashworth Scale and Ministry of Health Pain Scale. For feasibility assessment, adverse event and level of operator difficulty in handling the robotic glove device will be assessed. For cost effectiveness, the average cost per hour of a physiotherapist spend during training will be analyzed. Independent t-test or Mann-Whitney test will be used to compare the two mean scores and a P value of < 0.05 is considered statistically significant. Conclusion . We hypothesized that RGT group will show better improvement in terms of hand grip strength, pinch strength and hand motor function compared to CT group among post- stroke unilateral hand impairment patients. We also expected that RGT will be safe, feasible and cost effective.

Occupational therapists’ evaluation of the perceived usability and utility of wearable soft robotic exoskeleton gloves for hand function rehabilitation following a stroke

Purpose To evaluate the perceived usability and utility of using a soft robotic glove to rehabilitate hand function following a stroke. Methods A convergent parallel mixed-methods design was used to consult a convenience sample of 14 experienced occupational therapists (OTs) practicing within a specialised stroke rehabilitation program. All OTs participated in one 60-to-90-minute individual consultation during which the attributes of a recently-developed soft robotic glove (ExoGlove) were presented before they could test it on themselves. After this consultation, OTs completed the System Usability Scale (SUS) questionnaire and answered open-ended questions focussing on the usability and utility of soft robotic gloves framed according to the Unified Theory of Acceptance and Use of Technology (UTAUT). Results The OTs perceived the glove’s usability as being moderate-to-good on the SUS (median score= 63.75 on a scale of 100). Thematic analysis revealed the importance of specifically considering elements such as ease of use (e.g. simplicity and speed), cost, movement precision, durability, and safety, when developing soft robotic gloves such as the ExoGlove. Conclusions Engagement in a continuous improvement process is essential to maximise the perceived usability and utility of soft robotic gloves, particularly of the ExoGlove, through their final development phase before pilot testing their effects and effectiveness for post-stroke hand rehabilitation. Implications for rehabilitation All occupational therapists anticipate that a soft robotic glove such as the ExoGlove will allow them to increase treatment intensity and best aligns with principles of neuroplasticity. The clinical judgement and guidance of OTs, developed through practice, experience, and knowledge, remain essential to safely and efficiently exercise with a soft robotic glove. Achieving a balance between effort and performance expectancies is essential in developing and improving the functionality of soft robotic gloves, as with each additional functionality comes new challenges that impact its successful transition to a clinical setting.

Leveraging Human Perception in Robot Grasping and Manipulation Through Crowdsourcing and Gamification.

Robot grasping in unstructured and dynamic environments is heavily dependent on the object attributes. Although Deep Learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Furthermore, training such models requires large, difficult to obtain datasets. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation processes of robot grasping. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand the attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in two proof-of-concept applications: enhancing the control of a robotic exoskeleton glove and improving object identification for autonomous robot grasping. In addition, a model for estimating the framework response time is proposed. The obtained results demonstrate that the framework is capable of rapid adaptation to novel object classes, based purely on visual information and human experience.

Development and feasibility of a soft pneumatic-robotic glove to assist impaired hand function in quadriplegia patients: A pilot study

One of the common disorders in people with quadriplegia is having a weak grip strength that can affect activities of daily living (ADL). This study presents the design of a soft robotic glove via pneumatic actuators and feasibility according to a range of motion (ROM) of proximal interphalangeal (PIP) joint and user friendly.The soft robotic glove includes a neoprene cockup, two pumps, a controller that adjusts the pressure of the pumps, two-direction parts, and two silicone tubes placed on an index and middle fingers. A total of seven subjects (2healthy, 5quadriplegia patients) participated in this project. Performance of the device was verified through assessment in healthy participants first and then spinal cord injury (SCI) participants. The device evaluated the range of motion (ROM) of a proximal interphalangeal (PIP) joint. Then, subjects completed a satisfaction questionnaire.Results showed the ROM of the PIP joint (p value = 0.042) increased by using the robotic glove. The average score of the satisfaction questionnaire was 4.24 which was beyond the desirable threshold. In conclusion, the glove obtained ROM requirements to the grip usual objects and underlined the potential for assisting SCI participants in ADLs. Providing motion in all fingers should be investigated and developed in the future.

Angular Velocity Control of Pneumatic Soft Robotic Digits

Abstract The paper presents an angular velocity controller for the pneumatic soft robotic gloves which are applied to medical hand rehabilitation and performance augmentation. In order to configure the reliable control parameters, linear dynamic second-order state-space models of five robotic digits were identified by the experimental data. Using the identified models, proportional-integral (PI) control parameters were tuned by simulation studies. The tuned PI parameters were verified by experiment, which demonstrates that the system identification and simulation studies are reliable and can reduce the manual tuning time and prevent tuning hardware wear.