Sensitivity to Vibrotactile Stimulation in the Hand and Wrist: Effects of Motion, Temporal Patterns, and Biological Sex.

OBJECTIVE: We investigated the impact of low-tempo, repetitive hand movements on vibrotactile sensitivity by employing various temporal and spatial patterns in the hand and wrist area. BACKGROUND: The investigation of a human's ability to perceive vibrotactile stimuli during dynamic hand movements remains understudied, despite the prevalence of slow to mild hand motions in applications such as hand navigation or gesture control using haptic gloves in Virtual Reality (VR) and Augmented Reality (AR). METHOD: We investigated vibrotactile sensitivity, analyzing the impact of various factors, including Motion (static and low-tempo repetitive hand movements), Temporal Patterns (Single or Double vibrations with varying onset times), Tactor Placements (hand and wrist), Spatial Patterns, and Biological Sex. RESULTS: Our study revealed that Motion significantly influences vibrotactile sensitivity in the hand and wrist areas, leading to reduced accuracy rates during dynamic conditions. Additionally, as the stimulus onset approached in Double vibrations, accuracy rates markedly decreased. Notably, Hand Placement resulted in significantly higher accuracy rates compared to the Wrist Placement. CONCLUSION: Our findings underscore the impact of motion in reducing vibrotactile sensitivity on the back of the hand and around the wrist. APPLICATION: This research has wide-ranging practical applications, particularly in the field of VR/AR experiences, rehabilitation programs, and accessibility solutions through the use of haptic gloves. Insights from our study can be harnessed to enhance the efficacy of haptic gloves in conveying vibrotactile cues within these contexts.

The Effect of Time Pressure on Motion Economy and Smoothness of Interventional Radiology Trainee Performance in Simulated Central Venous Line Placement.

PURPOSE: To evaluate the effect of being under time pressure on procedural performance using hand motion analysis. MATERIALS AND METHODS: Eight radiology trainees performed central venous access on a phantom while recording video and hand motion data using an electromagnetic motion tracker. Each trainee performed the procedure six times: the first three trials without any prompts (control), while for the next three, they were asked to perform the task as fast as possible (time pressure). Validated hand motion metrics were analyzed, and two blinded and independent evaluators rated procedural performance using a previously validated task-specific global rating scale (GRS). Motion/time ratios and linear mixed-effect methods were used to control for time, and constants for both strategies were compared. RESULTS: Hand motion analysis showed that trainees completed the simulated procedure faster under time pressure (46 ± 18 s vs. 56 ± 27 s, p = 0.008) than during the control strategy. However, when controlling for time, trainees moved their hands 79 more centimeters (p = 0.04), made 15 more translational movements (p = 0.003) and 18 more rotational movements (p = 0.01) when under time pressure compared to at their own pace. CONCLUSION: Although trainees could perform the procedure faster under time pressure, there was a deterioration in hand motion economy and smoothness. This suggests that hand motion metrics offer a more comprehensive assessment of technical performance than time alone.

Hand Motion Analysis Using Accelerometer-Based Sensors and Sheep's Head Model for Basic Training in Functional Endoscopic Sinus Surgery.

INTRODUCTION:  Motion analysis, the study of movement patterns to evaluate performance, plays a crucial role in surgical training. It provides objective data that can be used to assess and improve trainee's precision, efficiency, and overall surgical technique. The primary aim of this study is to employ accelerometer-based sensors placed on the wrist to analyze hand motions during endoscopic sinus surgery training using the sheep's head. By capturing detailed movement data, the study seeks to quantify the motion characteristics that distinguish different levels of surgical expertise. This approach seeks to quantify motion characteristics indicative of surgical expertise and enhance the objectivity and effectiveness of surgical training feedback mechanisms. MATERIALS AND METHODS:  Twenty-four participants were divided into three groups based on their experience with endoscopic endonasal surgery. Each participant was tasked with performing specified procedures on an individual sheep's head, concentrating on exploring both nasal passages. A single Bluetooth Accelerometer WitMotion sensor was mounted on the dorsal surface of each hand. This facilitates the evaluation of efficiency parameters such as time, path length, and acceleration during the training procedures. Accelerometer data were collected and imported in CSV format (comma-separated values) for each group of surgeons-senior, specialist, and resident-mean values and standard deviations were computed. The Shapiro-Wilk Test assessed the normality of the distribution. The Kruskal-Wallis test was employed to compare procedural time, acceleration, and path length differences across the three surgeon experience levels. RESULTS:  For the procedural time, statistical significance appears in all surgical steps (p<0.001), with the biggest difference in the septoplasty group in favor of the senior group. A clear difference can be observed between the resulting acceleration of the dominant hands (instrument hand) and the non-dominant hand (endoscopic hand) and between the study groups. The difference between groups reaches statistical significance with a p-value <0.001. A statistically significant difference can be seen between the paths covered by each hand of every participant (p<0.001). Also, senior doctors covered significantly less movement with both hands than the specialists and the resident doctors (p<0.001). CONCLUSIONS:  The data show a clear learning curve from resident to senior, with residents taking more time and using more hand movements to complete the same tasks. Specialists are in the intermediate phase, showing signs of honing their technique towards efficiency. This comprehensive data set can help tailor training programs to focus on both efficiency (quicker procedures) and economy of motion (reduced path length and acceleration), especially in more complex procedures where the difference in performance is more pronounced.

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications.

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

Design of a Wearable Real-Time Hand Motion Tracking System Using an Array of Soft Polymer Acoustic Waveguides.

Robust hand motion tracking holds promise for improved human-machine interaction in diverse fields, including virtual reality, and automated sign language translation. However, current wearable hand motion tracking approaches are typically limited in detection performance, wearability, and durability. This article presents a hand motion tracking system using multiple soft polymer acoustic waveguides (SPAWs). The innovative use of SPAWs as strain sensors offers several advantages that address the limitations. SPAWs are easily manufactured by casting a soft polymer shaped as a soft acoustic waveguide and containing a commercially available small ceramic piezoelectric transducer. When used as strain sensors, SPAWs demonstrate high stretchability (up to 100%), high linearity (R2 > 0.996 in all quasi-static, dynamic, and durability tensile tests), negligible hysteresis (<0.7410% under strain of up to 100%), excellent repeatability, and outstanding durability (up to 100,000 cycles). SPAWs also show high accuracy for continuous finger angle estimation (average root-mean-square errors [RMSE] <2.00°) at various flexion-extension speeds. Finally, a hand-tracking system is designed based on a SPAW array. An example application is developed to demonstrate the performance of SPAWs in real-time hand motion tracking in a three-dimensional (3D) virtual environment. To our knowledge, the system detailed in this article is the first to use soft acoustic waveguides to capture human motion. This work is part of an ongoing effort to develop soft sensors using both time and frequency domains, with the goal of extracting decoupled signals from simple sensing structures. As such, it represents a novel and promising path toward soft, simple, and wearable multimodal sensors.

Quantification of motion during microvascular anastomosis simulation using machine learning hand detection.

OBJECTIVE: Microanastomosis is one of the most technically demanding and important microsurgical skills for a neurosurgeon. A hand motion detector based on machine learning tracking technology was developed and implemented for performance assessment during microvascular anastomosis simulation. METHODS: A microanastomosis motion detector was developed using a machine learning model capable of tracking 21 hand landmarks without physical sensors attached to a surgeon's hands. Anastomosis procedures were simulated using synthetic vessels, and hand motion was recorded with a microscope and external camera. Time series analysis was performed to quantify the economy, amplitude, and flow of motion using data science algorithms. Six operators with various levels of technical expertise (2 experts, 2 intermediates, and 2 novices) were compared. RESULTS: The detector recorded a mean (SD) of 27.6 (1.8) measurements per landmark per second with a 10% mean loss of tracking for both hands. During 600 seconds of simulation, the 4 nonexperts performed 26 bites in total, with a combined excess of motion of 14.3 (15.5) seconds per bite, whereas the 2 experts performed 33 bites (18 and 15 bites) with a mean (SD) combined excess of motion of 2.8 (2.3) seconds per bite for the dominant hand. In 180 seconds, the experts performed 13 bites, with mean (SD) latencies of 22.2 (4.4) and 23.4 (10.1) seconds, whereas the 2 intermediate operators performed a total of 9 bites with mean (SD) latencies of 31.5 (7.1) and 34.4 (22.1) seconds per bite. CONCLUSIONS: A hand motion detector based on machine learning technology allows the identification of gross and fine movements performed during microanastomosis. Economy, amplitude, and flow of motion were measured using time series data analysis. Technical expertise could be inferred from such quantitative performance analysis.

Two-stage classification of electromyogram signals from hand grasps in the transverse plane.

This paper presents a two-stage classification to resolve the effect of arm position changes on electromyogram (EMG) classification for hand grasps in the transverse plane. The proposed method combines the EMG signals with the signals from an inertial measurement unit in both the position and motion classification stages. To improve accuracy, we incorporate EMG data from the upper arm and shoulder with the forearm EMG signals. When evaluated on the five alternative object grasps placed on the nine positions, the proposed technique yields an average total classification error of 0.9%, which is a substantial improvement over the single-stage classification (4.3%).

Classification Models of Action Research Arm Test Activities in Post-Stroke Patients Based on Human Hand Motion.

The Action Research Arm Test (ARAT) presents a ceiling effect that prevents the detection of improvements produced with rehabilitation treatments in stroke patients with mild finger joint impairments. The aim of this study was to develop classification models to predict whether activities with similar ARAT scores were performed by a healthy subject or by a subject post-stroke using the extension and flexion angles of 11 finger joints as features. For this purpose, we used three algorithms: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The dataset presented class imbalance, and the classification models presented a low recall, especially in the stroke class. Therefore, we implemented class balance using Borderline-SMOTE. After data balancing the classification models showed significantly higher accuracy, recall, f1-score, and AUC. However, after data balancing, the SVM classifier showed a higher performance with a precision of 98%, a recall of 97.5%, and an AUC of 0.996. The results showed that classification models based on human hand motion features in combination with the oversampling algorithm Borderline-SMOTE achieve higher performance. Furthermore, our study suggests that there are differences in ARAT activities performed between healthy and post-stroke individuals that are not detected by the ARAT scoring process.

Case series: Two cases of severe retinal cicatricial contraction after vitrectomy for open-globe injury in patients with skin keloid.

PURPOSE: To report two cases of severe retinal cicatricial contraction after vitrectomy for open-globe injury in patients with skin keloid. OBSERVATIONS: One was a 33-year-old male patient who developed severe retinal cicatricial contraction 6.5 months post-operatively, and his skin wound was observed with keloid simultaneously. The second case was a 36-year-old male patient who developed recurrent retinal detachment 1 week after the two operations, and keloid was also found on his skin. CONCLUSIONS AND IMPORTANCE: Retinal detachment is a vision-threatening complication of open-globe injury. Besides most of the already known factors, skin keloid should be concerned.

Linear Relationships Among the Hand and Clubhead Motion Characteristics in Golf Driving in Skilled Male Golfers.

The purpose of this study was to investigate the linear relationships among the hand/clubhead motion characteristics in golf driving in skilled male golfers (n = 66; handicap ≤ 3). The hand motion plane (HMP) and functional swing plane (FSP) angles, the HMP-FSP angle gaps, the planarity characteristics of the off-plane motion of the clubhead, and the attack angles were computed from the drives captured by an optical motion capture system. The HMP angles were identified as the key variables, as the HMP and FSP angles were intercorrelated, but the plane angle gaps, the planarity bias, and the attack angles showed correlations to the HMP angles primarily. Three main swing pattern clusters were identified. The parallel HMP-FSP alignment pattern with a small direction gap was associated with neutral planarity and planar swing pattern. The inward alignment pattern with a large inward direction gap was characterized by flat planes, follow-through-centric planarity, spiral swing pattern, and inward/downward impact. The outward alignment pattern with a large outward direction gap was associated with steep planes, downswing-centric planarity, reverse spiral swing, and outward/upward impact. The findings suggest that practical drills targeting the hand motion pattern can be effective in holistically reprogramming the swing pattern.

A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm.

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0-50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.

Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection.

Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient's progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.

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.

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot.

(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human-robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human-robot collaboration applications to enhance the natural interaction between a human and a robot.

Utilization of Flexible-Wearable Sensors to Describe the Kinematics of Surgical Proficiency.

BACKGROUND: Traditional assessment (e.g., checklists, videotaping) for surgical proficiency may lead to subjectivity and does not predict performance in the clinical setting. Hand motion analysis is evolving as an objective tool for grading technical dexterity; however, most devices accompany with technical limitations or discomfort. We purpose the use of flexible wearable sensors to evaluate the kinematics of surgical proficiency. METHODS: Surgeons were recruited and performed a vascular anastomosis task in a single institution. A modified objective structured assessment of technical skills (mOSATS) was used for technical qualification. Flexible wearable sensors (BioStamp RCTM, mc10 Inc., Lexington, MA) were placed on the dorsum of the dominant hand (DH) and nondominant hand (nDH) to measure kinematic parameters: path length (Tpath), mean (Vmean) and peak (Vpeak) velocity, number of hand movements (Nmove), ratio of DH to nDH movements (rMov), and time of task (tTask) and further compared with the mOSATS score. RESULTS: Participants were categorized as experts (n = 12) and novices (n = 8) based on a cutoff mean mOSATS score. Significant differences for tTask (P = 0.02), rMov (P = 0.07), DH Tpath (P = 0.04), Vmean (P = 0.07), Vpeak (P = 0.04), and nDH Nmove (P = 0.02) were in favor of the experts. Overall, mOSATS had significant correlation with tTask (r = -0.69, P = 0.001), Nmove of DH (r = -0.44, P = 0.047) and nDH (r = -0.66, P = 0.001), and rMov (r = 0.52, P = 0.017). CONCLUSIONS: Hand motion analysis evaluated by flexible wearable sensors is feasible and informative. Experts utilize coordinated two-handed motion, whereas novices perform one-handed tasks in a hastily jerky manner. These tendencies create opportunity for improvement in surgical proficiency among trainees.

Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation.

The AnyBody Modeling System™ (AMS) is a musculoskeletal software simulation solution using inverse dynamics analysis. It enables the determination of muscle and joint forces for a given bodily motion. The recording of the individual movement and the transfer into the AMS is a complex and protracted process. Researches indicated that the contactless, visual Leap Motion Controller (LMC) provides clinically meaningful motion data for hand tracking. Therefore, the aim of this study was to integrate the LMC hand motion data into the AMS in order to improve the process of recording a hand movement. A Python-based interface between the LMC and the AMS, termed ROSE Motion, was developed. This solution records and saves the data of the movement as Biovision Hierarchy (BVH) data and AnyScript vector files that are imported into the AMS simulation. Setting simulation parameters, initiating the calculation automatically, and fetching results is implemented by using the AnyPyTools library from AnyBody. The proposed tool offers a rapid and easy-to-use recording solution for elbow, hand, and finger movements. Features include animation, cutting/editing, exporting the motion, and remote controlling the AMS for the analysis and presentation of musculoskeletal simulation results. Comparing the motion tracking results with previous studies, covering problems when using the LMC limit the correctness of the motion data. However, fast experimental setup and intuitive and rapid motion data editing strengthen the use of marker less systems as the herein presented compared to marker based motion capturing.

A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG.

Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about -2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.

The Influence of Aging on the Functional Connectivity of the Human Basal Ganglia.

Although basal ganglia (BG) are involved in the motor disorders of aged people, the effect of aging on the functional interaction of BG is not well-known. This work was aimed at studying the influence of aging on the functional connectivity of the motor circuit of BG (BGmC). Thirty healthy volunteers were studied (young-group 26.4 ± 5.7 years old; aged-group 63.1 ± 5.8 years old) with a procedure planned to prevent the spurious functional connectivity induced by the closed-loop arrangement of the BGmC. BG showed different functional interactions during the inter-task intervals and when subjects did not perform any voluntary task. Aging induced marked changes in the functional connectivity of the BGmC during these inter-task intervals. The finger movements changed the functional connectivity of the BG, these modifications were also different in the aged-group. Taken together, these data show a marked effect of aging on the functional connectivity of the BGmC, and these effects may be at the basis of the motor handicaps of aged people during the execution of motor-tasks and when they are not performing any voluntary motor task.

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.

Optimizing resource utilization during proficiency-based training of suturing skills in medical students: a randomized controlled trial of faculty-led, peer tutor-led, and holography-augmented methods of teaching.

BACKGROUND: Suturing is a fundamental skill in undergraduate medical education. It can be taught by faculty-led, peer tutor-led, and holography-augmented methods; however, the most educationally effective and cost-efficient method for proficiency-based teaching of suturing is yet to be determined. METHODS: We conducted a randomized controlled trial comparing faculty-led, peer tutor-led, and holography-augmented proficiency-based suturing training in pre-clerkship medical students. Holography-augmented training provided holographic, voice-controlled instructional material. Technical skill was assessed using hand motion analysis every ten sutures and used to construct learning curves. Proficiency was defined by one standard deviation within average faculty surgeon performance. Intervention arms were compared using one-way ANOVA of the number of sutures placed, full-length sutures used, time to proficiency, and incremental costs incurred. Surveys were used to evaluate participant preferences. RESULTS: Forty-four students were randomized to the faculty-led (n = 16), peer tutor-led (n = 14), and holography-augmented (n = 14) intervention arms. At proficiency, there were no differences between groups in the number of sutures placed, full-length sutures used, and time to achieve proficiency. The incremental costs of the holography-augmented method were greater than faculty-led and peer tutor-led instruction ($247.00 ± $12.05, p < 0.001) due to the high cost of the equipment. Faculty-led teaching was the most preferred method (78.0%), while holography-augmented was the least preferred (0%). 90.6% of students reported high confidence in performing simple interrupted sutures, which did not differ between intervention arms (faculty-led 100.0%, peer tutor-led 90.0%, holography-augmented 83.3%, p = 0.409). 93.8% of students felt the program should be offered in the future. CONCLUSION: Faculty-led and peer tutor-led instructional methods of proficiency-based suturing teaching were superior to holography-augmented method with respect to costs and participants' preferences despite being educationally equivalent.