A Wearable Detector for Simultaneous Finger Joint Motion Measurement

Study on the Design and Performance of a Glove Based on the FBG Array for Hand Posture Sensing

This study introduces a new wearable fiber-optic sensor glove. The glove utilizes a flexible material, polydimethylsiloxane (PDMS), and a silicone tube to encapsulate fiber Bragg gratings (FBGs). It is employed to enable the self-perception of hand posture, gesture recognition, and the prediction of grasping objects. The investigation employs the Support Vector Machine (SVM) approach for predicting grasping objects. The proposed fiber-optic sensor glove can concurrently monitor the motion of 14 hand joints comprising 5 metacarpophalangeal joints (MCP), 5 proximal interphalangeal joints (PIP), and 4 distal interphalangeal joints (DIP). To expand the measurement range of the sensors, a sinusoidal layout incorporates the FBG array into the glove. The experimental results indicate that the wearable sensing glove can track finger flexion within a range of 0° to 100°, with a modest minimum measurement error (Error) of 0.176° and a minimum standard deviation (SD) of 0.685°. Notably, the glove accurately detects hand gestures in real-time and even forecasts grasping actions. The fiber-optic smart glove technology proposed herein holds promising potential for industrial applications, including object grasping, 3D displays via virtual reality, and human-computer interaction.

Wearable Smart Fabric Based on Hybrid E-Fiber Sensor for Real-Time Finger Motion Detection

Wearable electronic sensors have attracted considerable interest in hand motion monitoring because of their small size, flexibility, and biocompatibility. However, the range of motion and sensitivity of many sensors are inadequate for complex and precise finger motion capture. Here, organic and inorganic materials were incorporated to fabricate a hybrid electronic sensor and optimized and woven into fabric for hand motion detection. The sensor was made from flexible porous polydimethylsiloxane (PDMS) filled with multiwalled carbon nanotubes (MWCNTs). The weight ratios of MWCNTs and geometric characteristics were optimized to improve the hybrid electronic sensor, which showed a high elongation at the breaking point (i.e., more than 100%) and a good sensitivity of 1.44. The strain-related deformation of the PDMS/MWCNT composite network resulted in a variation in the sensor resistance; thus, the strain level that corresponds to different finger motions is be calculated. Finally, the fabricated and optimized electronic sensor in filiform structure with a 6% MWCNT ratio was integrated with smart fabric to create a finger sleeve for real-time motion capture. In conclusion, a novel hybrid E-fiber sensor based on PDMS and MWCNTs was successfully fabricated in the current study with an optimal M/P ratio and structure, and textile techniques were adopted as new packaging approaches for such soft electronic sensors to create smart fabric for wearable and precise detection with highly enhanced sensing performance. The successful results in the current study demonstrate the great potential of such hybrid soft sensors in smart wearable healthcare management, including motion detection.

Finger Kinematics during Human Hand Grip and Release

A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development.

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review

BACKGROUND

Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one.

METHODS

We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry.

RESULTS

Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices.

CONCLUSION

Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera-based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

A system for automated acquisition of digital flexion using a 3-D camera and custom gantry

Introduction

Automated measurement of digital range of motion (ROM) may improve the accuracy of reporting and increase clinical efficiency. We hypothesize that a 3-D camera on a custom gantry will produce ROM measurements similar to those obtained with a manual goniometer.

Methods

A 3-D camera mounted on a custom gantry, was mechanized to rotate 200° around a platform. The video was processed to segment each digit and calculate joint angles in people with no history of any hand conditions or surgery to validate the system. A second-generation prototype was then assessed in people with different hand conditions. Metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joint flexion were measured repeatedly with a goniometer and the automated system. The average difference between manual and automatic measurements was calculated along with intraclass correlation coefficients (ICC).

Results

In the initial validation, 1,488 manual and 1,488 automated joint measurements were obtained and the measurement algorithm was refined. In people with hand conditions, 688 manual and 688 automated joint measurements were compared. Average acquisition time was 7 s per hand, with an additional 2-3 s required for data processing. ICC between manual and automated data in the clinical study ranged from 0.65 to 0.85 for the MCP joints, and 0.22 to 0.66 for PIP joints.

Discussion

The automated system resulted in rapid data acquisition, with reliability varying by type of joint and location. It has the potential to improve efficiency in the collection of physical exam findings. Further developments of the system are needed to measure thumb and distal phalangeal motions.

3D-Printed Soft Pneumatic Robotic Digit Based on Parametric Kinematic Model for Finger Action Mimicking

A robotic digit with shape modulation, allowing personalized and adaptable finger motions, can be used to restore finger functions after finger trauma or neurological impairment. A soft pneumatic robotic digit consisting of pneumatic bellows actuators as biomimetic artificial joints is proposed in this study to achieve specific finger motions. A parametric kinematic model is employed to describe the tip motion trajectory of the soft pneumatic robotic digit and guide the actuator parameter design (i.e., the pressure supply, actuator material properties, and structure requirements of the adopted pneumatic bellows actuators). The direct 3D printing technique is adopted in the fabrication process of the soft pneumatic robotic digit using the smart material of thermoplastic polyurethane. Each digit joint achieves different ranges of motion (ROM; bending angles of distal, proximal, and metacarpal joint are 107°, 101°, and 97°, respectively) under a low pressure of 30 kPa, which are consistent with the functional ROM of a human finger for performing daily activities. Theoretical model analysis and experiment tests are performed to validate the effectiveness of the digit parametric kinematic model, thereby providing evidence-based technical parameters for the precise control of dynamic pressure dosages to achieve the required motions.

Development and Operation of an Experimental System to Measure the Moments Generated in the Finger Joints

Little information is available on the forces that fingers can generate, and few devices exist to measure the forces they can create. The objective of this paper is to propose an experimental device to measure the moments generated by finger joints. The idea is to focus on a single joint and not on the effort generated by the whole finger. A system leaving only one joint free is developed to measure the maximum attainable moment in different joint positions between the extended and flexed finger. The device is tested on the proximal interphalangeal joints of the index fingers of thirty people for both hands. The results show a dispersion of results from one person to another but with similar trends in the evolution of the maximum achievable moment depending on the angle. Average values of the maximum moments attained by men and women for both hands are given for all angular positions of the joint. The results are analysed using principal component analysis. This analysis shows that four main modes represent more than 99% of the signal and allow the reconstruction of all the data for all the subjects. The four modes obtained can be used as a basis for the development of finger devices by hospital practitioners.

Analysis of length of finger segments with different hand postures to enhance glove design

It is important to understand how the hand and fingers elongate and contract with hand posture for optimally fitting and comfortable gloves. Nevertheless, the measurement and analysis of the finger segments for glove designs remain largely neglected. Here, the length and proportion of the finger segments when splayed and during gripping, and between the dorsal and palm sides of 30 participants are 3D scanned and analysed. The full digit lengths change by 7.6-11.9% with hand posture, but the finger segment changes are not proportional. The ratios of the fingertip to distal interphalangeal joint/full digit, and fingertip to the proximal interphalangeal joint/full digit, are important variables. The results are validated with 10 more subjects based on ratings of a ready-to-wear sports glove. Inaccurate proportioning of the finger regions causes shifting which results in displacement and discomfort. This research contributes to glove pattern engineering, with a focus on the finger segments.

Circulating Nurse Assistant: Non-Contact Body Centric Gesture Recognition Towards Reducing Latrogenic Contamination

Iatrogenic contamination causes serious health threats to both patients and healthcare staff. Contact operation is an important transmission route for nosocomial infection. Reducing direct contact during medical treatment can reduce nosocomial infection quickly and effectively. Scientific and technological progress in the 5G era brings new solutions to the problem of iatrogenic contamination. We conducted experiments at 27 GHz and 37 GHz to achieve contactless gesture recognition through the bornprint of body centric channel. The original channel S-parameters can achieve 82% (27 GHz) and 89% (37 GHz) basic recognition accuracy through simple statistical analysis. Basic switch recognition and multi-gesture selection recognition can meet the common operation requirements of circulating nurses, greatly reducing contact operations and reducing the probability of cross-contamination. Fully physically isolated body centric channel gesture sensing provides a new entry point for reducing iatrogenic contamination.

Low-Latency Haptic Open Glove for Immersive Virtual Reality Interaction

Recent advancements in telecommunications and the tactile Internet have paved the way for studying human senses through haptic technology. Haptic technology enables tactile sensations and control using virtual reality (VR) over a network. Researchers are developing various haptic devices to allow for real-time tactile sensation, which can be used in various industries, telesurgery, and other mission-critical operations. One of the main criteria of such devices is extremely low latency, as low as 1 ms. Although researchers are attempting to develop haptic devices with low latency, there remains a need to improve latency and robustness to hand sizes. In this paper, a low-latency haptic open glove (LLHOG) based on a rotary position sensor and min-max scaling (MMS) filter is proposed to realize immersive VR interaction. The proposed device detects finger flexion/extension and adduction/abduction motions using two position sensors located in the metacarpophalangeal (MCP) joint. The sensor data are processed using an MMS filter to enable low latency and ensure high accuracy. Moreover, the MMS filter is used to process object handling control data to enable hand motion-tracking. Its performance is evaluated in terms of accuracy, latency, and robustness to finger length variations. We achieved a very low processing delay of 145.37 μs per finger and overall hand motion-tracking latency of 4 ms. Moreover, we tested the proposed glove with 10 subjects and achieved an average mean absolute error (MAE) of 3.091∘ for flexion/extension, and 2.068∘ for adduction/abduction. The proposed method is therefore superior to the existing methods in terms of the above factors for immersive VR interaction.

Clip-On IMU System for Assessing Age-Related Changes in Hand Functions

Hand functions affect the instrumental activities of daily living. While functional outcome measures, such as a targeted box and block test, have been widely used in clinical settings and provide a useful measure of overall performance, the advent of a wearable Inertial Measurement Unit(IMU)-based system enables the examination of the specific performance and kinematic parameters of hand movements. This study proposed a novel clip-on IMU system to facilitate the clinically fitted measurements of fine-motor finger and wrist joint movements. Clinical validation was conducted with the aim of characterising age-related changes in hand functions, namely grasping, transporting, and releasing blocks. Eighteen young (age 20–31) and sixteen healthy older adults (age 75–89) were evaluated during the box and block test. The results demonstrated that an older age was characterized by slower movements and higher variations and kinematic alterations in the hand functions, such as a larger range of motions at the fingers as well as kinematic trajectories. The proposed IMU system and subsequent validations highlight the value of the performance and kinematics parameters for a more comprehensive understanding of fine-motor finger and wrist movements that could shed light on further implementations in clinical and practical settings.

The use of three-dimensional model construction of virtual technology in orthopedic treatment

Objective

The objective of this study is to explore the construction of a digital three-dimensional model of virtual technology that plays an auxiliary role in orthopedic treatment.

Methods

Three fracture patients were selected, with no abnormality was observed in bone examination, no musculoskeletal disease in the past; and spiral CT scan of the spine and pelvis, upper limbs, and lower limbs was performed. The virtual technology was used to build a digital 3D model, mainly using the editing software Mimics10.0 software. In addition, the virtual three-dimensional model was verified by virtual surgery, data storage security, work efficiency of the model, model validity, three-dimensional characteristics of the model, the interaction mode of the model, and the data accuracy of the model were studied.

Results

The digital 3D model was successfully established by Mimics10.0 software. The data fitting efficiency was very high. The data storage security of the 3D model was greatly improved compared with the 2D model, and the work efficiency was improved by at least 50%. There was also a significant change in the accuracy and interaction of data acquisition. Therefore, the detection of digital 3D model work through virtual surgery simulation fully demonstrated the positive auxiliary role of 3D model in orthopedic treatment.

Conclusion

The digital 3D model based on Mimics10.0 software is efficient and accurate in obtaining data. It is very effective for subsequent adjuvant therapy in the field of orthopedics, reducing the probability of misdiagnosis by doctors, saving time and improving efficiency, reducing patient's physical pain and unnecessary economic expenses.

Wearable Hand Module and Real-Time Tracking Algorithms for Measuring Finger Joint Angles of Different Hand Sizes with High Accuracy using FBG Strain Sensor

This paper presents a wearable hand module which was made of five fiber Bragg grating (FBG) strain sensor and algorithms to achieve high accuracy even when worn on different hand sizes of users. For real-time calculation with high accuracy, FBG strain sensors move continuously according to the size of the hand and the bending of the joint. Representatively, four algorithms were proposed; point strain (PTS), area summation (AREA), proportional summation (PS), and PS/interference (PS/I or PS/I_α). For more accurate and efficient assessments, 3D printed hand replica with different finger sizes was adopted and quantitative evaluations were performed for index~little fingers (77 to 117 mm) and thumb (68~78 mm). For index~little fingers, the optimized algorithms were PS and PS/I_α. For thumb, the optimized algorithms were PS/I_α and AREA. The average error angle of the wearable hand module was observed to be 0.47 ± 2.51° and mean absolute error (MAE) was achieved at 1.63 ± 1.97°. These results showed that more accurate hand modules than other glove modules applied to different hand sizes can be manufactured using FBG strain sensors which move continuously and algorithms for tracking this movable FBG sensors.

Analysis of Interventionalists' Natural Behaviors for Recognizing Motion Patterns of Endovascular Tools During Percutaneous Coronary Interventions

Many robotic platforms can indeed reduce radiation exposure to clinicians during percutaneous coronary intervention (PCI), however, interventionalists' natural manipulations are rarely involved in robot-assisted PCI. This requires more attention to analyze interventionalists' natural behaviors during conventional PCI. In this study, four types of natural behavior (i.e., muscle activity, hand motion, proximal force, and finger motion) were synchronously acquired from ten subjects while performing six typical types of guidewire manipulation. These behaviors are evaluated by a hidden Markov model (HMM) based analysis framework for relevant behavior selection. Relevant behaviors are further used as the input of two HMM-based classification frameworks to recognize guidewire motion patterns. Experimental results show that under the basic classification framework (BCF), 91.01% and 93.32% recognition accuracies can be achieved by using all behaviors and relevant behaviors, respectively. Furthermore, the hierarchical classification framework can significantly enhance the recognition ability of relevant behaviors with an accuracy of 96.39%. These promising results demonstrate great potential of proposed methods for promoting the future design of human-robot interfaces in robot-assisted PCI.

A Human-Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

This paper presents a human-machine interface that establishes a link between the user and a hand prosthesis. It successfully uses electrical impedance tomography, a conventional bio-impedance imaging technique, using an array of electrodes contained in a wristband on the user's forearm. Using a high-performance analog front-end application specific integrated circuit (ASIC), the user's forearm inner bio-impedance redistribution is accurately assessed. These bio-signatures are strongly related to hand motions and using artificial neural networks, they can be learned so as to recognize the user's intention in real time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation with a gesture switching enabled sub-grouping method. Experiments with five subjects show that the system can achieve 98.5% accuracy with a grouping of three gestures and an accuracy of 94.4% with two sets of five gestures. The ASIC comprises a current driver with common-mode reduction capability and a current feedback instrumentation amplifier (that occupy an area of 0.07 mm²). The ASIC operates from ±1.65 V power supplies and has a minimum bio-impedance sensitivity of 12.7 mΩ_p-p.