Dual-Task Upper Extremity Motor Performance Measured by Video Processing as Cognitive-Motor Markers for Older Adults.

INTRODUCTION: The use of dual-task model such as dual-task gait has been extensively studied to assess cognitive-motor performance among older adults. However, space restriction and safety factor limit its applications in remote assessment. To address the gap, we propose a video processing-based approach to remotely quantify cognitive-motor performance using a 20-s repetitive elbow flexion-extension test with dual-task condition, called video-based motoric-cognitive meter (MCM). METHODS: Eighteen older participants (age: 78.6 ± 6.5 years) who were clinically diagnosed as having either mild cognitive impairment or dementia were included in this study. Participants were asked to perform 20-s repetitive elbow flexion-extension exercise with a memory exercise by counting backward from a two-digit number. During the test, all movements of the forearm were recorded by a video camera. As a comparator, a validated wrist-worn sensor was used, which allowed quantifying upper extremity kinematics. RESULTS: The results showed a good agreement (r ≥ 0.530 and ICC2,1 ≥ 0.681) between the derived dual-task upper extremity motor performance from the proposed video-based MCM and a clinically validated sensor-based MCM. We also observed moderate correlations (r ≥ 0.496) between some measures of video-based MCM (flexion time, extension time, and flexion-extension time) and clinical cognitive scale (Mini-Mental State Examination [MMSE]). Additionally, some measures of dual-task upper extremity motor performance (speed, flexion time, extension time, and flexion-extension time) were associated with dual-task gait speed (r ≥ 0.557), which has been found to be correlated with cognitive impairment. Lastly, the selected dual-task motor performance metric (flexion time) was sensitive to predict MMSE scores in linear regression analyses with statistical significance (adjusted R2 = 0.306, p = 0.025). CONCLUSION: This study proposes a video processing-based approach to analyze dual-task upper extremity motor performance from a simple and convenient upper extremity function test. The results indicate concurrent validity of the proposed video-based MCM compared with the sensor-based MCM, and associations between dual-task upper extremity motor performance and clinically validated cognitive markers (MMSE scores and dual-task gait). Future studies are warranted to explore sensitivity of this solution to promote remote assessment of cognitive-motor performance among older adults in telehealth applications.

Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults.

Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical application of ultrasound is limited by the expertise required for image interpretation. This study aimed to develop a deep learning algorithm to identify the presence and location of hemoperitoneum on POCUS to assist novice clinicians in accurate interpretation of the Focused Assessment with Sonography in Trauma (FAST) exam. We analyzed right upper quadrant (RUQ) FAST exams obtained from 94 adult patients (44 confirmed hemoperitoneum) using the YoloV3 object detection algorithm. Exams were partitioned via fivefold stratified sampling for training, validation, and hold-out testing. We assessed each exam image-by-image using YoloV3 and determined hemoperitoneum presence for the exam using the detection with highest confidence score. We determined the detection threshold as the score that maximizes the geometric mean of sensitivity and specificity over the validation set. The algorithm had 95% sensitivity, 94% specificity, 95% accuracy, and 97% AUC over the test set, significantly outperforming three recent methods. The algorithm also exhibited strength in localization, while the detected box sizes varied with a 56% IOU averaged over positive cases. Image processing demonstrated only 57-ms latency, which is adequate for real-time use at the bedside. These results suggest that a deep learning algorithm can rapidly and accurately identify the presence and location of free fluid in the RUQ of the FAST exam in adult patients with hemoperitoneum.

Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease.

INTRODUCTION: An early detection of impaired functional performance is critical to enhance symptom management for patients with chronic obstructive pulmonary disease (COPD). However, conventional functional measures based on walking assessments are often impractical for small clinics where the available space to administrate gait-based test is limited. This study examined the feasibility and effectiveness of an upper-extremity frailty meter (FM) in identifying digital measures of functional performance and assessing frailty in COPD patients. METHODS: Forty-eight patients with COPD (age = 68.8 ± 8.5 years, body mass index [BMI] = 28.7 ± 5.8 kg/m2) and 49 controls (age = 70.0 ± 3.0 years, BMI = 28.7 ± 6.1 kg/m2) were recruited. All participants performed a 20-s repetitive elbow flexion-extension test using a wrist-worn FM sensor. Functional performance was quantified by FM metrics, including speed (slowness), range of motion (rigidity), power (weakness), flexion and extension time (slowness), as well as speed and power reduction (exhaustion). Conventional functional measures, including timed-up-and-go test, gait and balance tests, and 5 repetition sit-to-stand test, were also performed. RESULTS: Compared to controls, COPD patients exhibited deteriorated performances in all conventional functional assessments (d = 0.64-1.26, p < 0.010) and all FM metrics (d = 0.45-1.54, p < 0.050). FM metrics had significant agreements with conventional assessment tools (|r| = 0.35-0.55, p ≤ 0.001). FM metrics efficiently identified COPD patients with pre-frailty and frailty (d = 0.82-2.12, p < 0.050). CONCLUSION: This study proposes the feasibility of using a 20-s repetitive elbow flexion-extension test and wrist-worn sensor-derived frailty metrics as an alternative and practical solution to evaluate functional performance in COPD patients. Its simplicity and low risk for test administration may also facilitate its application for remote patient monitoring. Furthermore, in settings where the administration of walking test is impractical, for example, when ventilator support is needed or space is limited, FM may be used as an alternative solution. Future studies are encouraged to use the FM to quantitatively monitor the progressive decline in functional performance and quantify outcomes of rehabilitation interventions.

Digital Biomarkers for the Objective Assessment of Disability in Neurogenic Thoracic Outlet Syndrome.

Neurogenic thoracic outlet syndrome (nTOS) is a musculoskeletal disorder in which compression of the brachial plexus between the scalene muscles of the neck and the first rib results in disabling upper extremity pain and paresthesia. Currently there are no objective metrics for assessing the disability of nTOS or for monitoring response to its therapy. We aimed to develop digital biomarkers of upper extremity motor capacity that could objectively measure the disability of nTOS using an upper arm inertial sensor and a 20-s upper extremity task that provokes nTOS symptoms. We found that digital biomarkers of slowness, power, and rigidity statistically differentiated the affected extremities of patients with nTOS from their contralateral extremities (n = 16) and from the extremities of healthy controls (n = 13); speed and power had the highest effect sizes. Digital biomarkers representing slowness, power, and rigidity correlated with patient-reported outcomes collected with the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire and the visual analog scale of pain (VAS); speed had the highest correlation. Digital biomarkers of exhaustion correlated with failure of physical therapy in treating nTOS; and digital biomarkers of slowness, power, and exhaustion correlated with favorable response to nTOS surgery. In conclusion, sensor-derived digital biomarkers can objectively assess the impairment of motor capacity resultant from nTOS, and correlate with patient-reported symptoms and response to therapy.

Assessing Laparoscopic Surgical Skills Using Similarity Network Models: A Pilot Study.

Background: Medical devices are becoming more complex, and doctors need to learn quickly how to use new medical tools. However, it is challenging to objectively assess the fundamental laparoscopic surgical skill level and determine skill readiness for advancement. There is a lack of objective models to compare performance between medical trainees and experienced doctors. Methods: This article discusses the use of similarity network models for individual tasks and a combination of tasks to show the level of similarity between residents and medical students while performing each task and their overall laparoscopic surgical skill level using a medical device (eg laparoscopic instruments). When a medical student is connected to most residents, that student is competent to the next training level. Performance of sixteen participants (5 residents and 11 students) while performing 3 tasks in 3 different training schedules is used in this study. Results: The promising result shows the general positive progression of students over 4 training sessions. Our results also indicate that students with different training schedules have different performance levels. Students' progress in performing a task is quicker if the training sessions are held more closely compared to when the training sessions are far apart in time. Conclusions: This study provides a graph-based framework for evaluating new learners' performance on medical devices and their readiness for advancement. This similarity network method could be used to classify students' performance using similarity thresholds, facilitating decision-making related to training and progression through curricula.

Evaluation of Augmented Reality Feedback in Surgical Training Environment.

Providing computer-based laparoscopic surgical training has several advantages that enhance the training process. Self-evaluation and real-time performance feedback are 2 of these advantages, which avoid dependency of trainees on expert feedback. The goal of this study was to investigate the use of a visual time indicator as real-time feedback correlated with the laparoscopic surgical training. Twenty novices participated in this study working with (and without) different presentations of time indicators. They performed a standard peg transfer task, and their completion times and muscle activity were recorded and compared. Also of interest was whether the use of this type of feedback induced any side effect in terms of motivation or muscle fatigue. RESULTS: Of the 20 participants, 15 (75%) preferred using a time indicator in the training process rather than having no feedback. However, time to task completion showed no significant difference in performance with the time indicator; furthermore, no significant differences in muscle activity or muscle fatigue were detected with/without time feedback. CONCLUSION: The absence of significant difference between task performance with/without time feedback shows that using visual real-time feedback can be included in surgical training based on user preference. Trainees may benefit from this type of feedback in the form of increased motivation. The extent to which this can influence training frequency leading to performance improvement is a question for further study.

Objective assessment of postural ergonomics in neurosurgery: integrating wearable technology in the operating room.

OBJECTIVE: Physical stress associated with the static posture of neurosurgeons over prolonged periods can result in fatigue and musculoskeletal disorders. Objective assessment of surgical ergonomics may contribute to postural awareness and prevent further complications. This pilot study examined the feasibility of using wearable technology as a biofeedback tool to address this gap. METHODS: Ten neurosurgeons, including 5 attendings (all faculty) and 5 trainees (1 fellow, 4 residents), were recruited and equipped with two wearable sensors attached to the back of their head and their upper back. The sensors collected the average time spent in extended (≤ -10°), neutral (> -10° and < 10°), and flexed (≥ 10°) static postures (undetected activity for more than 10 seconds) during spine and cranial procedures. Feasibility outcomes aimed for more than 70% of accurate data collection. Exploratory outcomes included the comparison of postural variability within and between participants adjusted to their demographics excluding nonrelated surgical activities, and postoperative self-assessment surveys. RESULTS: Sixteen (80%) of 20 possible recordings were successfully collected and analyzed from 11 procedures (8 spine, 3 cranial). Surgeons maintained a static posture during 52.7% of the active surgical time (mean 1.58 hrs). During spine procedures, all surgeons used an exoscope while standing, leading to a significantly longer time spent in a neutral static posture (p < 0.001, partial η2 = 0.14): attendings remained longer in a neutral static posture (36.4% ± 15.3%) than in the extended (9% ± 6.3%) and flexed (5.7% ± 3.4%) static postures; trainees also remained longer in a neutral static posture (30.2% ± 13.8%) than in the extended (11.1% ± 6.3%) and flexed (11.9% ± 6.6%) static postures. During cranial procedures, surgeons intermittently transitioned between standing/exoscope use and sitting/microscope use, with trainees spending a shorter time in a neutral static posture (16.3% vs 48.5%, p < 0.001) and a longer time in a flexed static posture (18.5% vs 2.7%, p < 0.001) compared with attendings. Additionally, longer cranial procedures correlated with surgeons spending a longer time (r = 0.94) in any static posture (extended, flexed, and neutral), with taller surgeons exhibiting longer periods in flexed and extended static postures (r = 0.86). Postoperative self-assessment revealed that attendings perceived spine procedures as more difficult than trainees (p = 0.029), while trainees found cranial procedures to be of greater difficulty than spine procedures (p = 0.012). Attendings felt more stressed (p = 0.048), less calmed (p = 0.024), less relaxed (p = 0.048), and experienced greater stiffness in their upper body (p = 0.048) and more shoulder pain (p = 0.024) during cranial versus spine procedures. CONCLUSIONS: Wearable technology is feasible to assess postural ergonomics and provide objective biofeedback to neurosurgeons during spine and cranial procedures. This study showed reproducibility for future comparative protocols focused on correcting posture and surgical ergonomic education.

Machine Learning Algorithm Detection of Confluent B-Lines.

OBJECTIVE: B-lines are a ring-down artifact of lung ultrasound that arise with increased alveolar water in conditions such as pulmonary edema and infectious pneumonitis. Confluent B-line presence may signify a different level of pathology compared with single B-lines. Existing algorithms aimed at B-line counting do not distinguish between single and confluent B-lines. The objective of this study was to test a machine learning algorithm for confluent B-line identification. METHODS: This study used a subset of 416 clips from 157 subjects, previously acquired in a prospective study enrolling adults with shortness of breath at two academic medical centers, using a hand-held tablet and a 14-zone protocol. After exclusions, random sampling generated a total of 416 clips (146 curvilinear, 150 sector and 120 linear) for review. A group of five experts in point-of-care ultrasound blindly evaluated the clips for presence/absence of confluent B-lines. Ground truth was defined as majority agreement among the experts and used for comparison with the algorithm. RESULTS: Confluent B-lines were present in 206 of 416 clips (49.5%). Sensitivity and specificity of confluent B-line detection by algorithm compared with expert determination were 83% (95% confidence interval [CI]: 0.77-0.88) and 92% (95% CI: 0.88-0.96). Sensitivity and specificity did not statistically differ between transducers. Agreement between algorithm and expert for confluent B-lines measured by unweighted κ was 0.75 (95% CI: 0.69-0.81) for the overall set. CONCLUSION: The confluent B-line detection algorithm had high sensitivity and specificity for detection of confluent B-lines in lung ultrasound point-of-care clips, compared with expert determination.

Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health.

Remote screening physical frailty (PF) may assist in triaging patients with chronic obstructive pulmonary disease (COPD) who are in clinical priorities to visit a clinical center for preventive care. Conventional PF assessment tools have however limited feasibility for remote patient monitoring applications. To improve the safety of PF assessment, we previously developed and validated a quick and safe PF screening tool called Frailty Meter (FM). FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion/extension task using a wrist-worn sensor and generates a frailty index (FI) ranging from zero to one; higher values indicate progressively greater severity of frailty. However, the use of wrist-sensor limits its applications in telemedicine and remote patient monitoring. In this study, we developed a sensor-less FM based on deep learning-based image processing, which can be easily integrated into mobile health and enables remote assessment of physical frailty. The sensor-less FM extracts kinematic features of the forearm motion from the video of 20-second elbow flexion and extension recorded by a tablet camera, and then calculates frailty phenotypes and FI. To test the validity of sensor-less FM, 11 COPD patients admitted to a Telehealth pulmonary rehabilitation clinic and 10 healthy young volunteers (controls) were recruited. All participants completed the test indicating high feasibility. Strong correlations (0.72 < r < 0.99) were observed between the sensor-based FM and sensor-less FM to extract all frailty phenotypes and FI. After adjusting with age and body mass index(BMI), sensor-less FM enables distinguishing COPD group from controls (p<0.050) with the largest effect sizes observed for weakness (Cohen's effect size d=2.24), frailty index (d=1.70), and slowness (d=1.70). These pilot findings suggest feasibility and proof of concept validity of this sensor-less FM toward remote assessment of PF in COPD patients.

Human-exoskeleton control simulation, kinetic and kinematic modeling and parameters extraction.

Exoskeletons are new robotic systems that are in close contact with the human body. Thus, their performances are influenced by many factors, including the selection of its structure, actuators, measurement devices, parameters, and mechanism of coupling to the human body. The latter offers numerous challenges to its design, evaluation and modification, including analyzing the effectiveness of the exoskeleton, finding the optimal force for actuators and, discovering the effect of changes in design parameters on human muscle behavior, which are very difficult to measure. Therefore, numerical simulations play an important role in solving these challenges and have the potential to improve treatment strategies and medical decision-making. In this study, a simulation-based method is presented for the designing and analysis of the parameters of an exoskeleton and its wearer's kinetics and kinematics. Model-based design software, including OpenSim and Inventor, and mathematical software, such as MATLAB, are integrated. This method can assist in the modification of exoskeleton devices and allow physiologists, neuroscientists, and physical therapists to generate new solutions for rehabilitation programs using exoskeletons. •Using the movements parameters of each individual subject in her/his exoskeleton design.•Combining the power of OpenSim body movement and the ability of Matlab in mathematical calculations.•Considering the effect of exoskeleton parameters on each muscle-skeleton movement.

The Effect of Daily Use of Plantar Mechanical Stimulation Through Micro-Mobile Foot Compression Device Installed in Shoe Insoles on Vibration Perception, Gait, and Balance in People With Diabetic Peripheral Neuropathy.

OBJECTIVE: People with diabetic peripheral neuropathy (DPN) have impaired gait and balance performance. The aim of this study is to investigate therapeutic effectiveness of mechanical stimulation through a wearable foot compression device equipped in a shoe insole on vibration perception, balance control and gait performance in people with DPN. METHODS: Using a single-arm 4-week intervention study design, we examined effectiveness of daily use of shoes equipped with the foot compression device (Footbeat™, AVEX, Grand Junction, CO, USA) on improving vibration perception threshold (VPTmax), skin perfusion pressure (SPP), ankle brachial index (ABI), lower extremities edema (circumferences in the calf and ankle), and motor performance (postural sway with eyes open and closed conditions, and gait performance during normal, dual-task and fast walking). Thirty people with type 2 diabetes and symptoms of PN completed the experimental protocol. RESULTS: Improvements in VPTmax (before = 27.4 V, after = 23.3 V, P = .007, d = 0.33, where d denotes effect size), center-of-mass sway in the mediolateral direction with both eyes open and closed conditions (before = 0.94 cm, after = 0.76 cm, P = .020, d = 0.47; before = 1.10 cm, after = 0.83 cm, P = .033, d = 0.66, respectively), and stride velocity for normal walking, dual-task walking and fast walking tasks (before = 0.87 m/s, after = 0.96 m/s, P = .017, d = 0.41; before = 0.75 m/s, after = 0.91 m/s, P = .001, d = 0.77; before = 1.10 m/s, after = 1.20 m/s, P = .043, d = 0.33, respectively) were found post treatment. There was no significant improvement in SPP, ABI, and circumferences in the calf and ankle. CONCLUSIONS: Our findings suggest the wearable foot compression device may be effective for reducing neuropathic symptoms and enhancing motor performances in people with DPN.

Using wearables to screen motor performance deterioration because of cancer and chemotherapy-induced peripheral neuropathy (CIPN) in adults - Toward an early diagnosis of CIPN.

OBJECTIVE: An essential component for optimizing quality of life in adults with cancer is determining the degree to which therapy may negatively impact motor-performance, so that patients can maintain their quality of life and independence. This study examined whether instrumented gait and balance could determine the magnitude of deterioration in motor-performance from chemotherapy-induced peripheral neuropathy (CIPN). METHODS: We recruited 84 adults with cancer (age = 71.1 ±â€¯9.7 years old, BMI = 26.8 ±â€¯6.2 kg/m2, gender = 56%female) and 57 age-matched non-cancer patients (age = 69.5 ±â€¯9.8 years old, BMI = 27.1 ±â€¯6.0 kg/m2, gender = 79%female). Based on clinical screening, the group with cancer was classified into two groups: participants with CIPN (CIPN+) and without CIPN (CIPN-). Gait and balance were quantified using validated wearables. The Vibration Perception Threshold (VPT) test was used to stratify the CIPN+ group into mild (Mild-CIPN) and severe (Severe-CIPN) subgroups. RESULTS: All gait and balance parameters were deteriorated in the group with cancer compared to non-cancer group with the largest effects observed for stride-time (11%, Cohen's effect size d = 1.00, p < 0.001) and eyes-closed ankle sway (94%, d = 0.49, p = 0.001). The same trend was observed when the Severe-CIPN subgroup was compared to the Mild-CIPN. VPT correlates significantly with motor deterioration, with the largest correlation found in stride-time (Rho = 0.37, p = 0.007). Severe-CIPN subjects were significantly older and overall had more deterioration in the majority of motor-performance parameters after adjusting for age (p < 0.050). CONCLUSION: These results confirmed the negative impact of CIPN on motor-performance with the largest effects on ankle stability and stride-time. VPT is a predictor of motor deterioration and may be used to determine the severity of CIPN symptom.

Virtual Reality Training System for Anytime/Anywhere Acquisition of Surgical Skills: A Pilot Study.

This article presents a hardware/software simulation environment suitable for anytime/anywhere surgical skills training. It blends the advantages of physical hardware and task analogs with the flexibility of virtual environments. This is further enhanced by a web-based implementation of training feedback accessible to both trainees and trainers. Our training system provides a self-paced and interactive means to attain proficiency in basic tasks that could potentially be applied across a spectrum of trainees from first responder field medical personnel to physicians. This results in a powerful training tool for surgical skills acquisition relevant to helping injured warfighters.