Vision-Based Human-Machine Interface for an Assistive Robotic Exoskeleton Glove.

This paper presents a vision-based Human-Machine Interface (HMI) for an assistive exoskeleton glove, designed to incorporate force planning capabilities. While Electroencephalogram (EEG) and Electromyography (EMG)-based HMIs allow direct grasp force planning via user signals, voice and vision-based HMIs face limitations. In particular, two primary force planning methods encounter issues in these HMIs. First, traditional force optimization struggles with unfamiliar objects due to lack of object information. Second, the slip-grasp method faces a high failure rate due to inadequate initial grasp force. To address these challenges, this paper introduces a vision-based HMI to estimate the initial grasp forces of the target object. The initial grasp force estimation is performed based on the size and surface material of the target object. The experimental results demonstrate a grasp success rate of 87. 5%, marking significant improvements over the slip-grasp method (71.9%).

Design, Control, and Experimental Evaluation of A Novel Robotic Glove System for Patients with Brachial Plexus Injuries.

This paper presents the development of an exoskeleton glove system for people who suffer from brachial plexus injuries, aiming to assist their lost grasping functionality. The robotic system consists of a portable glove system and an embedded controller. The glove system consists of Linear Series Elastic Actuators (LSEA), Rotary Series Elastic Actuators (RSEA), and optimized finger linkages to provide imitated human motion to each finger and a coupled motion of the hand. The design principles and optimization strategies were investigated to balance functionality, portability, and stability. The model-based force control strategy compensated with a backlash model and model-free force control strategy are presented and compared. Results show that our proposed model-free control method achieves the goal of accurate force control. Finally, experiments were conducted with the prototype of the developed integrated exoskeleton glove system. Results from 3 subjects with 150 trials show that our proposed exoskeleton glove system has the potential to be used as a rehabilitation device for patients.

DESIGN, ANALYSIS, AND PROTOTYPING OF A NOVEL SINGLE DEGREE-OF-FREEDOM INDEX FINGER EXOSKELETON MECHANISM.

This paper presents a novel index finger exoskeleton mechanism for patients who suffer from brachial plexus injuries, which takes advantage of our previously proposed rigid coupling hybrid mechanism (RCHM) concept used for robotic tail mechanisms. The core idea of this concept is to drive the (i+1)-th link using the motions of the i-th link, instead of the traditional way of transmitting motion directly from the base. This specific configuration allows designing a single degree of freedom (DOF) bending mechanism using a low-profile rack and pinion mechanism and makes the proposed exoskeleton system compact, lightweight, and portable, which are highly desired features for daily usages of exoskeleton gloves. The mechanism is optimized to mimic the grasping motions of human fingers and the sensitivity analysis of its critical design variables is then conducted to explore the performance of the optimization results. The results show that for the current design, the tip position accuracy is mainly affected by the distance between the rack and the corresponding joints. A proof-of-concept prototype was built to verify the novel mobility of the proposed mechanism and to evaluate its performance on a human finger. The index finger exoskeleton experiments demonstrate the new mechanism's ability to grasp small objects.

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 biometric authentication using 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.

Development of a Novel Low-profile Robotic Exoskeleton Glove for Patients with Brachial Plexus Injuries.

This paper presents the design and development of a novel, low-profile, exoskeleton robotic glove aimed for people who suffer from brachial plexus injuries to restore their lost grasping functionality. The key idea of this new glove lies in its new finger mechanism that takes advantage of the rigid coupling hybrid mechanism (RCHM) concept. This mechanism concept couples the motions of the adjacent human finger links using rigid coupling mechanisms so that the overall mechanism motion (e.g., bending, extension, etc.) could be achieved using fewer actuators. The finger mechanism utilizes the single degree of freedom case of the RCHM that uses a rack-and-pinion mechanism as the rigid coupling mechanism. This special arrangement enables to design each finger mechanism of the glove as thin as possible while maintaining mechanical robustness simultaneously. Based on this novel finger mechanism, a two-finger low-profile robotic glove was developed. Remote center of motion mechanisms were used for the metacarpophalangeal (MCP) joints. Kinematic analysis and optimization-based kinematic synthesis were conducted to determine the design parameters of the new glove. Passive abduction/adduction joints were considered to improve the grasping flexibility. A proof-of-concept prototype was built and pinch grasping experiments of various objects were conducted. The results validated the mechanism and the mechanical design of the new robotic glove and demonstrated its functionalities and capabilities in grasping objects with various shapes and weights that are used in activities of daily living (ADLs).

Development and Experimental Evaluation of a Novel Portable Haptic Robotic Exoskeleton Glove System for Patients with Brachial Plexus Injuries.

This paper presents the development and experimental evaluation of a portable haptic exoskeleton glove system designed for people who suffer from brachial plexus injuries to restore their lost grasping functionality. The proposed glove system involves force perception, linkage-driven finger mechanism, and personalized voice control to achieve various grasping functionality requirements. The fully integrated system provides our wearable device with lightweight, portable, and comfortable characterization for grasping objects used in daily activities. Rigid articulated linkages powered by Series Elastic Actuators (SEAs) with slip detection on the fingertips provide stable and robust grasp for multiple objects. The passive abduction-adduction motion of each finger is also considered to provide better grasping flexibility for the user. The continuous voice control with bio-authentication also provides a hands-free user interface. The experiments with different objects verify the functionalities and capabilities of the proposed exoskeleton glove system in grasping objects with various shapes and weights used in activities of daily living (ADLs).

Dynamic Modeling, Analysis, and Design Synthesis of a Reduced Complexity Quadruped with a Serpentine Robotic Tail.

Serpentine tail structures are widely observed in the animal kingdom and are thought to help animals to handle various motion tasks. Developing serpentine robotic tails and using them on legged robots has been an attractive idea for robotics. This article presents the theoretical analysis for such a robotic system that consists of a reduced complexity quadruped and a serpentine robotic tail. Dynamic model and motion controller are formulated first. Simulations are then conducted to analyze the tail's performance on the airborne righting and maneuvering tasks of the quadruped. Using the established simulation environment, systematic analyses on critical design parameters, namely, the tail mounting point, tail length, torso center of mass (COM) location, tail-torso mass ratio, and the power consumption distribution, are performed. The results show that the tail length and the mass ratio influence the maneuvering angle the most while the COM location affects the landing stability the most. Based on these design guidelines, for the current robot design, the optimal tail parameters are determined as a length of two times as long as the torso length and a weight of 0.09 times as heavy as the torso weight.

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.

Design and Analysis of a Variable Inertia Spatial Robotic Tail for Dynamic Stabilization.

This paper presents the design of a four degree-of-freedom (DoF) spatial tail and demonstrates the dynamic stabilization of a bipedal robotic platform through a hardware-in-loop simulation. The proposed tail design features three active revolute joints with an active prismatic joint, the latter of which provides a variable moment of inertia. Real-time experimental results validate the derived mathematical model when compared to simulated reactive moment results, both obtained while executing a pre-determined trajectory. A 4-DoF tail prototype was constructed and the tail dynamics, in terms of reactive force and moments, were validated using a 6-axis load cell. The paper also presents a case study where a zero moment point (ZMP) placement-based trajectory planner, along with a model-based controller, was developed in order for the tail to stabilize a simulated unstable biped robot. The case study also demonstrates the capability of the motion planner and controller in reducing the system's kinetic energy during periods of instability by maintaining ZMP within the support polygon of the host biped robot. Both experimental and simulation results show an improvement in the tail-generated reactive moments for robot stabilization through the inclusion of prismatic motion while executing complex trajectories.

Grasp Prediction Toward Naturalistic Exoskeleton Glove Control.

This paper presents accurate grasp prediction algorithms that can be used for naturalistic, synergistic control of exoskeleton gloves with minimal user input. Recent research in exoskeleton systems has focused mainly on the development of novel soft or hard mechanical designs and actuation systems for rehabilitative and assistive applications. On the other hand, estimating user intent for intelligent grasp assistance is a problem that has remained largely unaddressed. As demonstrated by existing studies, the complex motions of human hand can be mapped to a latent space, thereby reducing perceived noise in individual joint angles as well as the number of variables upon which the prediction must be performed. To this extent, we present two latent space grasp prediction algorithms for intelligent exoskeleton glove control. The first presented algorithm is based on a linear regression to determine the slope and prediction horizon. The second algorithm is based on a Gaussian process trajectory matching where the trajectory of the grasping motion is probabilistically compared to existing data in order to form a prediction. Both algorithms were tested on published motion data collected from healthy subjects. In addition, the experimental validation of the algorithms was done using the RML glove (Robotics and Mechatronics Lab), which yielded similar prediction accuracy as compared to the simulation results. The proposed prediction algorithm can act as the backbone for a shifting authority controller that simultaneously amplifies the user's motion while guiding them toward their desired grasp. Preliminary work in this direction is also described in the paper, with directions for future research.

INTEGRATED AND CONFIGURABLE VOICE ACTIVATION AND SPEAKER VERIFICATION SYSTEM FOR A ROBOTIC EXOSKELETON GLOVE.

Efficient human-machine interface (HMI) for exoskeletons remains an active research topic, where sample methods have been proposed including using computer vision, EEG (electroencephalogram), and voice recognition. However, some of these methods lack sufficient accuracy, security, and portability. This paper proposes a HMI referred as integrated trigger-word configurable voice activation and speaker verification system (CVASV). The CVASV system is designed for embedded systems with limited computing power that can be applied to any exoskeleton platform. The CVASV system consists of two main sections, including an API based voice activation section and a deep learning based text-independent voice verification section. These two sections are combined into a system that allows the user to configure the activation trigger-word and verify the user's command in real-time.

A NOVEL DESIGN OF A ROBOTIC GLOVE SYSTEM FOR PATIENTS WITH BRACHIAL PLEXUS INJURIES.

This paper presents the design of an exoskeleton glove system for people who suffer from the brachial plexus injuries in an effort to restore their lost grasping functionality. The robotic system consists of an embedded controller and a portable glove system. The glove system consists of Linear Series Elastic Actuators (SEA), Rotary SEA and optimized finger linkages to provide motion to each finger and a coupled motion of the hand and the wrist. The design is based on various functionality requirements such as being lightweight and portable for activities of daily living, especially for grasping. The contact force at each fingertip and bending angle of each finger are measured for future implementation of intelligent control algorithms for autonomous grasping. To provide better flexibility and comfort for the users, abduction and adduction of each finger as well as flexion of the thumb were taken into consideration in the design. The glove system is adjustable for different hand sizes. The micro-controllers and batteries are integrated on the forearm in order to provide a completely portable design solution.

Stable Grasp Control With a Robotic Exoskeleton Glove.

An exoskeleton robotic glove intended for patients who have suffered paralysis of the hand due to stroke or other factors has been developed and integrated. The robotic glove has the potential to aid patients with grasping objects as part of their daily life activities. Grasp stability was studied and researched by various research groups, but mainly focused on robotic grippers by devising conditions for a stable grasp of objects. Maintaining grasp stability is important so as to reduce the chances of the object slipping and dropping. But there was little focus on the grasp stability of robotic exoskeleton gloves, and most of the research was focused on mechanical design. A robotic exoskeleton glove was developed as well as novel methods to improve the grasp stability. The glove is constructed with rigidly coupled four-bar linkages attached to the finger tips. Each linkage mechanism has one-DOF (degree of freedom) and is actuated by a linear series elastic actuator (SEA). Two methods were developed to satisfy two of the conditions required for a stable grasp. These include deformation prevention of soft objects, and maintaining force and moment equilibrium of the objects being grasped. Simulations were performed to validate the performance of the proposed algorithms. A battery of experiments was performed on the integrated prototype in order to validate the performance of the algorithms developed.

A General Purpose Robotic Hand Exoskeleton With Series Elastic Actuation.

This paper describes the design and control of a novel hand exoskeleton. A subcategory of upper extremity exoskeletons, hand exoskeletons have promising applications in healthcare services, industrial workplaces, virtual reality, and military. Although much progress has been made in this field, most of the existing systems are position controlled and face several design challenges, including achieving minimal size and weight, difficulty enforcing natural grasping motions, exerting sufficient grip strength, ensuring the safety of the users hand, and maintaining overall user friendliness. To address these issues, this paper proposes a novel, slim, lightweight linkage mechanism design for a hand exoskeleton with a force control paradigm enabled via a compact series elastic actuator. A detailed design overview of the proposed mechanism is provided, along with kinematic and static analyses. To validate the overall proposed hand exoskeleton system, a fully integrated prototype is developed and tested in a series of experimental trials.

A SERIES ELASTIC ACTUATOR DESIGN AND CONTROL IN A LINKAGE BASED HAND EXOSKELETON.

This paper presents the design of a series elastic actuator and a higher level controller for said actuator to assist the motion of a user's hand in a linkage based hand exoskeleton. While recent trends in the development of exoskeleton gloves has been to exploit the advantages of soft actuators, their size and power requirements limit their adoption. On the other hand, a series elastic actuator can provide compliant assistance to the wearer while remaining compact and lightweight. Furthermore, the linkage based mechanism integrated with the SEA offers repeatability and accuracy to the hand exoskeleton. By measuring the user's motion intention through compression of the elastic elements in the actuator, a virtual dynamic system can be utilized that assists the users in performing the desired motion while ensuring the motion stability of the overall system. This work describes the detailed design of the actuator followed by performance tests using a simple PD controller on the integrated robotic exoskeleton prototype. The performance of the proposed high level controller is tested using the integrated exoskeleton glove mechanism for a single finger, using two types of input motion. Preliminary results are discussed as well as plans for integrating the proposed actuator and high level controller into a complete hand exoskeleton prototype to perform intelligent grasping.

Maneuvering and stabilization control of a bipedal robot with a universal-spatial robotic tail.

This paper analyzes control methodologies to implement maneuvering and stabilization behaviors in a bipedal robot using a bioinspired robotic tail. Looking to nature, numerous animals augment their legs' functionality using a tail nature, numerous animals augment their legs' functionality using a tail to assist with both maneuvering and stabilization; looking to the robotics literature, previous research primarily focuses on single-mass, pendulum-like tails designed to perform a specific task. The overarching goal of this research is to study how bioinspired tail designs may be used in conjunction with low-complexity leg designs to achieve high-performance behaviors. In pursuit of this goal, this paper connects the serpentine universal-spatial robotic tail (USRT) with a biped consisting of a pair of Robotic Modular Legs to study the outer- and inner-loop control considerations necessary to achieve yaw-angle turning and stable leg lifting. The design and modeling of the tail and leg subsystems are presented, along with considerations for sensing the USRT's configuration in real-time. In addition, two inner-loop controllers that map desired tail trajectories into actuation commands are presented: a prescribed velocity approach that only utilizes motor feedback, and a prescribed torque approach that incorporates both feedforward consideration of the tail dynamics and feedback consideration from the tail sensing. Two outer-loop controllers-one for yaw-angle steering (maneuvering), and one for roll-angle disturbance rejection when lifting a foot (stabilization)-are also defined. Case studies including simulation and experimental results are used to validate the outer-loop control approaches.

Intelligent Object Grasping With Sensor Fusion for Rehabilitation and Assistive Applications.

This paper presents the design and control of the intelligent sensing and force-feedback exoskeleton robotic glove to create a system capable of intelligent object grasping initiated by detection of the user's intentions through motion amplification. Using a combination of sensory feedback streams from the glove, the system has the ability to identify and prevent object slippage, as well as adapting grip geometry to the object properties. The slip detection algorithm provides updated inputs to the force controller to prevent an object from being dropped, while only requiring minimal input from a user who may have varying degrees of functionality in their injured hand. This paper proposes the use of a high dynamic range, low cost conductive elastomer sensor coupled with a negative force derivative trigger that can be leveraged in order to create a controller that can intelligently respond to slip conditions through state machine architecture, and improve the grasping robustness of the exoskeleton. The improvements to the previous design are described while the details of the controller design and the proposed assistive and rehabilitative applications are explained. Experimental results confirming the validity of the proposed system are presented. Finally, this paper concludes with topics for future exploration.

Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove.

This paper presents a hand rehabilitation learning system, the SAFE Glove, a device that can be utilized to enhance the rehabilitation of subjects with disabilities. This system is able to learn fingertip motion and force for grasping different objects and then record and analyze the common movements of hand function including grip and release patterns. The glove is then able to reproduce these movement patterns in playback fashion to assist a weakened hand to accomplish these movements, or to modulate the assistive level based on the user's or therapist's intent for the purpose of hand rehabilitation therapy. Preliminary data have been collected from healthy hands. To demonstrate the glove's ability to manipulate the hand, the glove has been fitted on a wooden hand and the grasping of various objects was performed. To further prove that hands can be safely driven by this haptic mechanism, force sensor readings placed between each finger and the mechanism are plotted. These experimental results demonstrate the potential of the proposed system in rehabilitation therapy.

Sensing and Force-Feedback Exoskeleton (SAFE) Robotic Glove.

This paper presents the design, implementation and experimental validation of a novel robotic haptic exoskeleton device to measure the user's hand motion and assist hand motion while remaining portable and lightweight. The device consists of a five-finger mechanism actuated with miniature DC motors through antagonistically routed cables at each finger, which act as both active and passive force actuators. The SAFE Glove is a wireless and self-contained mechatronic system that mounts over the dorsum of a bare hand and provides haptic force feedback to each finger. The glove is adaptable to a wide variety of finger sizes without constraining the range of motion. This makes it possible to accurately and comfortably track the complex motion of the finger and thumb joints associated with common movements of hand functions, including grip and release patterns. The glove can be wirelessly linked to a computer for displaying and recording the hand status through 3D Graphical User Interface (GUI) in real-time. The experimental results demonstrate that the SAFE Glove is capable of reliably modeling hand kinematics, measuring finger motion and assisting hand grasping motion. Simulation and experimental results show the potential of the proposed system in rehabilitation therapy and virtual reality applications.