Comparison Between a Single-Lead ECG Garment Device and a Holter Monitor: A Signal Quality Assessment.

Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.

A Wireless Multimodal Physiological Monitoring ASIC for Animal Health Monitoring Injectable Devices.

Utilizing injectable devices for monitoring animal health offers several advantages over traditional wearable devices, including improved signal-to-noise ratio (SNR) and enhanced immunity to motion artifacts. We present a wireless application-specific integrated circuit (ASIC) for injectable devices. The ASIC has multiple physiological sensing modalities including body temperature monitoring, electrocardiography (ECG), and photoplethysmography (PPG). The ASIC fabricated using the CMOS 180 nm process is sized to fit into an injectable microchip implant. The ASIC features a low-power design, drawing an average DC power of 155.3 µW, enabling the ASIC to be wirelessly powered through an inductive link. To capture the ECG signal, we designed the ECG analog frontend (AFE) with 0.3 Hz low cut-off frequency and 45-79 dB adjustable midband gain. To measure PPG, we employ an energy-efficient and safe switched-capacitor-based (SC) light emitting diode (LED) driver to illuminate an LED with milliampere-level current pulses. A SC integrator-based AFE converts the current of photodiode with a programmable transimpedance gain. A resistor-based Wheatstone Bridge (WhB) temperature sensor followed by an instrumentation amplifier (IA) provides 27-47 °C sensing range with 0.02 °C inaccuracy. Recorded physiological signals are sequentially sampled and quantized by a 10-bit analog-to-digital converter (ADC) with the successive approximation register (SAR) architecture. The SAR ADC features an energy-efficient switching scheme and achieves a 57.5 dB signal-to-noise-and-distortion ratio (SNDR) within 1 kHz bandwidth. Then, a back data telemetry transmits the baseband data via a backscatter scheme with intermediate-frequency assistance. The ASIC's overall functionality and performance has been evaluated through an in vivo experiment.

Brno University of Technology Smartphone PPG Database (BUT PPG): Annotated Dataset for PPG Quality Assessment and Heart Rate Estimation.

To the best of our knowledge, there is no annotated database of PPG signals recorded by smartphone publicly available. This article introduces Brno University of Technology Smartphone PPG Database (BUT PPG) which is an original database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating photoplethysmographic (PPG) signal quality and estimation of heart rate (HR). The data comprises 48 10-second recordings of PPGs and associated electrocardiographic (ECG) signals used for determination of reference HR. The data were collected from 12 subjects (6 female, 6 male) aged between 21 and 61. PPG data were collected by smartphone Xiaomi Mi9 with sampling frequency of 30 Hz. Reference ECG signals were recorded using a mobile ECG recorder (Bittium Faros 360) with a sampling frequency of 1,000 Hz. Each PPG signal includes annotation of quality created manually by biomedical experts and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably, and thus, these signals are unsuitable for further analysis. As the only available database containing PPG signals recorded by smartphone, BUT PPG is a unique tool for the development of smart, user-friendly, cheap, on-the-spot, self-home-monitoring of heart rate with the potential of widespread using.

Smart automated heart health monitoring using photoplethysmography signal classification.

This paper proposes a smart, automated heart health-monitoring (SAHM) device using a single photoplethysmography (PPG) sensor that can monitor cardiac health. The SAHM uses an Orthogonal Matching Pursuit (OMP)-based classifier along with low-rank motion artifact removal as a pre-processing stage. Major contributions of the proposed SAHM device over existing state-of-the-art technologies include these factors: (i) the detection algorithm works with robust features extracted from a single PPG sensor; (ii) the motion compensation algorithm for the PPG signal can make the device wearable; and (iii) the real-time analysis of PPG input and sharing through the Internet. The proposed low-cost, compact and user-friendly PPG device can also be prototyped easily. The SAHM system was tested on three different datasets, and detailed performance analysis was carried out to show and prove the efficiency of the proposed algorithm.

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis.

PURPOSE: Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nuclei, glands, lymphocytes). One application of DP is in telepathology, which involves digitally transmitting DP slides over the Internet for secondary diagnosis by an expert at a remote location. Unfortunately, the places benefiting most from telepathology often have poor Internet quality, resulting in prohibitive transmission times of DP images. Image compression may help, but the degree to which image compression affects performance of DL algorithms has been largely unexplored. METHODS: We investigated the effects of image compression on the performance of DL strategies in the context of 3 representative use cases involving segmentation of nuclei (n = 137), segmentation of lymph node metastasis (n = 380), and lymphocyte detection (n = 100). For each use case, test images at various levels of compression (JPEG compression quality score ranging from 1-100 and JPEG2000 compression peak signal-to-noise ratio ranging from 18-100 dB) were evaluated by a DL classifier. Performance metrics including F1 score and area under the receiver operating characteristic curve were computed at the various compression levels. RESULTS: Our results suggest that DP images can be compressed by 85% while still maintaining the performance of the DL algorithms at 95% of what is achievable without any compression. Interestingly, the maximum compression level sustainable by DL algorithms is similar to where pathologists also reported difficulties in providing accurate interpretations. CONCLUSION: Our findings seem to suggest that in low-resource settings, DP images can be significantly compressed before transmission for DL-based telepathology applications.

Exploring the promise of broadband fisheries echosounders for species discrimination with quantitative assessment of data processing effects.

It remains an open question how well the increased bandwidth afforded by broadband echosounders can improve species discrimination in fisheries acoustics. Here, an objective statistical approach was used to determine if there is information available in dual channel broadband data (45-170 kHz) to allow discrimination between in situ echoes obtained from monospecific aggregations of three species (hake, Merluccius productus: anchovy, Engraulis mordax; and krill, Euphausiia pacifica) using a remotely operated vehicle. These data were used to explore the effects of processing choices on the ability to statistically classify the broadband spectra to species. This ability was affected by processing choices including the Fourier transform analysis window size, available bandwidth, and the method and scale of data averaging. The approach to normalizing the spectra and the position of individual targets in the beam, however, had little effect. Broadband volume backscatter and single target spectra were both used to successfully classify acoustic data from these species with ∼6% greater success using volume backscatter data. Broadband data were effectively classified to species while simulated multi-frequency narrowband data were categorized at rates near chance, supporting the presumption that greater bandwidth increases the information available for the characterization and classification of biological targets.

Mobile Electrocardiogram Monitoring and Health-Related Quality of Life in Patients With Atrial Fibrillation: Findings From the iPhone Helping Evaluate Atrial Fibrillation Rhythm Through Technology (iHEART) Study.

BACKGROUND: Atrial fibrillation (AF) is associated with high recurrence rates and poor health-related quality of life (HRQOL) but few effective interventions to improve HRQOL exist. OBJECTIVE: The aim of this study was to examine the impact of the "iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology" (iHEART) intervention on HRQOL in patients with AF. METHODS: We randomized English- and Spanish-speaking adult patients with AF to receive either the iHEART intervention or usual care for 6 months. The iHEART intervention used smartphone-based electrocardiogram monitoring and motivational text messages. Three instruments were used to measure HRQOL: the Atrial Fibrillation Effect on Quality of Life (AFEQT), the 36-item Short-Form Health survey, and the EuroQol-5D. We used linear mixed models to compare the effect of the iHEART intervention on HRQOL, quality-adjusted life-years, and AF symptom severity. RESULTS: A total of 238 participants were randomized to the iHEART intervention (n = 115) or usual care (n = 123). Of the participants, 77% were men and 76% were white. More than half (55%) had an AF recurrence. Both arms had improved scores from baseline to follow-up for AFEQT and AF symptom severity scores. The global AFEQT score improved 18.5 and 11.2 points in the intervention and control arms, respectively (P < .05). There were no statistically significant differences in HRQOL, quality-adjusted life-years, or AF symptom severity between groups. CONCLUSIONS: We found clinically meaningful improvements in AF-specific HRQOL and AF symptom severity for both groups. Additional research with longer follow-up should examine the influence of smartphone-based interventions for AF management on HRQOL and address the unique needs of patients diagnosed with different subtypes of AF.

Assessment of signal quality measured with a smart 12-lead ECG acquisition T-shirt.

BACKGROUND: Ambulatory ECG monitoring is typically achieved using portable devices with limited number of surface leads, autonomy, and length of recording. Smart garments with multiple conductive textile electrodes provide great promise to perform continuous and comfortable ECG monitoring. METHODS: We evaluated the ECG signal quality measured on healthy subjects with a smart 12-lead ECG acquisition T-shirt or a 12-lead Holter recording. ECG signals were recorded during 3 min with both techniques in three resting positions (supine, seated, standing) and while walking. Three readers independently assessed ECG patterns and evaluated the denoising of the isoelectric line, the distinction of p waves, R peaks and RR intervals, and the possible appreciation of cardiac rhythm in at least 3 leads. RESULTS: Thirty healthy subjects (70% males, 29.5 ± 7.8 years) were enrolled in the study. For all three resting conditions, cardiac rhythm was appreciated in 100% of recordings with distinction of p waves, R peaks, and isoelectric line in >97% of recordings. Appreciation of cardiac rhythm was lower in the walking conditions with both techniques (53.3% vs. 46.7%, Holter vs. smart T-shirt, p = .60) mainly due to difficulties to distinguish p waves. These results were consistent across both genders. All ECG parameters (heart rate, PR, QRS and QTC intervals) were comparable between both techniques. No skin irritation was seen with the textile electrodes. CONCLUSIONS: A smart T-shirt with 13 textiles electrodes allows short-duration 12-lead ECG acquisition with quality levels comparable to Holter recordings. The novel device should now be evaluated for long-term non-invasive ECG monitoring.

Comparison of a Pragmatic and Regression Approach for Wearable EEG Signal Quality Assessment.

Wearable electroencephalogram (EEG) solutions allow portability and real-time measurements in uncontrolled conditions. For reliable and reproducible interpretation of the EEG data, it is essential to accurately identify EEG segments contaminated by artefacts. Two data quality indicator approaches are proposed: pragmatic and regression based. The former extracts statistical features and applies data-driven thresholding, while the latter uses a regression model on the same set of statistical features to predict data quality. The performance of the approaches is validated against EEG data recorded during uncontrolled laboratory and free-living conditions, and compared to a validated approach. The proposed approaches achieve average accuracy of over [Formula: see text] in detecting artefactual data, which is higher than the FORCe signal quality estimation method ([Formula: see text]). The main strength of the proposed algorithms is in the significant increase of specificity over the state-of-the-art. The two models perform equally across different databases. Training of the two approaches on free-living conditions data showed better generalization when tested on different types of databases, i.e., uncontrolled laboratory and free-living. Although the accuracy in determining artefact-contaminated data is highest when using a window size of 8 s, the accuracy drop is minor when using shorter window size, demonstrating another advantage over existing methods. Given low complexity of both pragmatic and regression approach, it facilitates a real-time implementation, which is demonstrated using a wearable EEG headset system available at IMEC.

Assessment of Motor Impairments in Early Untreated Parkinson's Disease Patients: The Wearable Electronics Impact.

OBJECTIVE: The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing objective measures of motor abnormalities. However, up to now, those sensors have been used on advanced PD patients with evident motor impairment. As a novelty, here we report the impact of wearable sensors in the evaluation of motor abnormalities in newly diagnosed, untreated, namely de novo, patients. METHODS: A network of wearable sensors was used to measure motor capabilities, in 30 de novo PD patients and 30 healthy subjects, while performing five motor tasks. Measurement data were used to determine motor features useful to highlight impairments and were compared with the corresponding clinical scores. Three classifiers were used to differentiate PD from healthy subjects. RESULTS: Motor features gathered from wearable sensors showed a high degree of significance in discriminating the early untreated de novo PD patients from the healthy subjects, with 95% accuracy. The rates of severity obtained from the measured features are partially in agreement with the clinical scores, with some highlighted, though justified, exceptions. CONCLUSION: Our findings support the feasibility of adopting wearable sensors in the detection of motor anomalies in early, untreated, PD patients. SIGNIFICANCE: This work demonstrates that subtle motor impairments, occurring in de novo patients, can be evidenced by means of wearable sensors, providing clinicians with instrumental tools as suitable supports for early diagnosis, and subsequent management.

ARTSENS® Pen-portable easy-to-use device for carotid stiffness measurement: technology validation and clinical-utility assessment.

OBJECTIVE: The conventional medical imaging modalities used for arterial stiffness measurement are non-scalable and unviable for field-level vascular screening. The need for an affordable, easy-to-operate automated non-invasive technologies remains unmet. To address this need, we present a portable image-free ultrasound device-ARTSENS® Pen, that uses a single-element ultrasound transducer for carotid stiffness evaluation. APPROACH: The performance of the device was clinically validated on a cohort of 523 subjects. A clinical-grade B-mode ultrasound imaging system (ALOKA eTracking) was used as the reference. Carotid stiffness measurements were taken using the ARTSENS® Pen in sitting posture emulating field scenarios. MAIN RESULTS: A statistically significant correlation (r > 0.80, p < 0.0001) with a non-significant bias was observed between the measurements obtained from the two devices. The ARTSENS® Pen device could perform highly repeatable measurements (with variation smaller than 10%) on a relatively larger percentage of the population when compared to the ALOKA system. The study results also revealed the sensitivity of ARTSENS® Pen to detect changes in arterial stiffness with age. SIGNIFICANCE: The easy-to-use technology and the automated algorithms of the ARTSENS® Pen make it suitable for cardiovascular risk assessment in resource-constrained settings.

Design of a Flexible Wearable Smart sEMG Recorder Integrated Gradient Boosting Decision Tree Based Hand Gesture Recognition.

This paper proposed a wearable smart sEMG recorder integrated gradient boosting decision tree (GBDT) based hand gesture recognition. A hydrogel-silica gel based flexible surface electrode band is used as the tissue interface. The sEMG signal is collected using a neural signal acquisition analog front end (AFE) chip. A quantitative analysis method is proposed to balance the algorithm complexity and recognition accuracy. A parallel GBDT implementation is proposed featuring a low latency. The proposed GBDT based neural signal processing unit (NSPU) is implemented on an FPGA near the AFE. A RF module is used for wireless communication. A hand gesture set including 12 gestures is designed for human-computer interaction. Experimental results show an overall hand gesture recognition accuracy of 91%.

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG.

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

Noninvasive Assessment of Jugular Venous Pressure via Force-Coupled Single Crystal Ultrasound.

OBJECTIVE: we have developed a handheld device for noninvasive quantitative assessment of jugular venous pressure (JVP). METHODS: we used a single crystal ultrasound coupled to a force-sensing load cell to measure JVP based on the force necessary to collapse the internal jugular vein (IJV) walls. We used a gelatin-based model system of the IJV to test the ability of single crystal ultrasound to identify the IJV and verified the cross-sectional position and diameter of the vessels with conventional imaging ultrasound. We also tested our prototype device on healthy human volunteers. RESULTS: experiments on model system demonstrated that vessel diameters determined with single crystal ultrasound were in close agreement with the diameters derived from conventional 2-D ultrasound. Proof-of-concept human experiments demonstrate that single crystal ultrasound can detect the IJV in basal and collapsed states, as compared to gold-standard sonography (insert stats). Assessment of JVP in human volunteers was physiologically consistent with and sensitive to postural changes (supine JVP 6.6 ± 2.4 mmHg; standing JVP 4.2 ± 1.9 mmHg (p < 0.0001). CONCLUSION: noninvasive assessment of JVP could prove valuable in informing rapid clinical decision-making across various pathologies and conditions leading to derangements in intravascular volume status.

Hidden Markov Model-Based Fall Detection With Motion Sensor Orientation Calibration: A Case for Real-Life Home Monitoring.

Falls are a major threat for senior citizens' independent living. Motion sensor technologies and automatic fall detection systems have emerged as a reliable low-cost solution to this challenge. We develop a hidden Markov model (HMM) based fall detection system to detect falls automatically using a single motion sensor for real-life home monitoring scenarios. We propose a new representation for acceleration signals in HMMs to avoid feature engineering and developed a sensor orientation calibration algorithm to resolve sensor misplacement issues (misplaced sensor location and misaligned sensor orientation) in real-world scenarios. HMM classifiers are trained to detect falls based on acceleration signal data collected from motion sensors. We collect a dataset from experiments of simulated falls and normal activities and acquired a dataset from a real-world fall repository (FARSEEING) to evaluate our system. Our system achieves positive predictive value of 0.981 and sensitivity of 0.992 on the experiment dataset with 200 fall events and 385 normal activities, and positive predictive value of 0.786 and sensitivity of 1.000 on the real-world fall dataset with 22 fall events and 2618 normal activities. Our system's results significantly outperform benchmark systems, which shows the advantage of our HMM-based fall detection system with sensor orientation calibration. Our fall detection system is able to precisely detect falls in real-life home scenarios with a reasonably low false alarm ratet.

Toward Wireless Health Monitoring via an Analog Signal Compression-Based Biosensing Platform.

Wireless all-analog biosensor design for the concurrent microfluidic and physiological signal monitoring is presented in this paper. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by the analog joint source-channel coding (AJSCC). Two circuit designs are discussed, including the stacked-voltage-controlled voltage source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of printed circuit board realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.

In-Ear Audio Wearable: Measurement of Heart and Breathing Rates for Health and Safety Monitoring.

OBJECTIVE: This paper examines the integration of a noninvasive vital sign monitoring feature into the workers' hearing protection devices (HPDs) by using a microphone positioned within the earcanal under the HPD. METHODS: 25 test-subjects were asked to breathe at various rhythms and intensities and these realistic sound events were recorded in the earcanal. Digital signal processing algorithms were then developed to assess heart and breathing rates. Finally, to test the robustness of theses algorithms in noisy work environments, industrial noise was added to the in-ear recorded signals and an adaptive denoising filter was used. RESULTS: The developed algorithms show an absolute mean error of 4.3 beats per minute (BPM) and 2.7 cycles per minute (CPM). The mean difference estimate is -0.44 BPM with a limit of agreement (LoA) interval of -14.3 to 13.4 BPM and 2.40 CPM with a LoA interval of -2.62 to 7.48 CPM. Excellent denoising is achieved with the adaptive filter, able to cope with ambient sound pressure levels of up to 110 dB SPL, resulting in a small error for heart rate detection, but a much larger error for breathing rate detection. CONCLUSION: Extraction of the heart and breathing rates from an acoustical measurement in the occluded earcanal under an HPD is possible and can even be conducted in the presence of a high level of ambient noise. SIGNIFICANCE: This proof of concept enables the development of a wide range of noninvasive health and safety monitoring audio wearables for industrial workplaces and life-critical applications where HPDs are used.

Validation of Static and Dynamic Balance Assessment Using Microsoft Kinect for Young and Elderly Populations.

Reduction in balance is an indicator of fall risk, and therefore, an accurate and cost-effective balance assessment tool is essential for prescribing effective postural control strategies. This study established the validity of the Kinect v2 sensor in assessing center of mass (CoM) excursion and velocity during single-leg balance and voluntary ankle sway tasks among young and elderly subjects. We compared balance outcome measures (anteroposterior (AP) and mediolateral (ML) CoM excursion and velocity and average sway length) to a traditional three-dimensional motion analysis system. Twenty subjects (10 young: age = y, height cm, weight kg; 10 elderly: age y, height cm, weight kg), with no history of lower extremity injury, participated in this study. Subjects performed six randomized trials; four single-leg stand (SLS) and two ankle sway trials. SLS and voluntary ankle sway trials showed that consistency (ICC(2, k)) and agreement (ICC(3, k)) for all variables when all subjects were considered, as well as when the elderly and young groups were analyzed separately. Concordance between systems ranged from poor to nearly perfect depending on the group, task, and variable assessed.

The Technology of Processed Electroencephalogram Monitoring Devices for Assessment of Depth of Anesthesia.

Commercial brain function monitors for depth of anesthesia have been available for more than 2 decades; there are currently more than 10 devices on the market. Advances in this field are evidenced by updated versions of existing monitors, development of new monitors, and increasing research unveiling the mechanisms of anesthesia on the brain. Electroencephalography signal processing forms an integral part of the technology supporting the brain function monitors for derivation of a depth-of-anesthesia index. This article aims to provide a better understanding of the technology and functionality behind these monitors. This review will highlight the general design principles of these devices and the crucial stages in electroencephalography signal processing and classification, with a focus on the key mathematical techniques used in algorithm development for final derivation of the index representing anesthetic state. We will briefly discuss the advantages and limitations of this technology in the clinical setting as a tool in our repertoire used for optimizing individualized patient care. Also included is a table describing 10 available commercial depth-of-anesthesia monitors.

Dynamic Functional Assessment of Hand Motion Using an Animation Glove: The Effect of Stenosing Tenosynovitis.

BACKGROUND: The aim of the present study is to determine whether an animation glove can be utilized to provide a reliable and reproducible assessment of dynamic hand function and whether this assessment is altered in the setting of hand pathology. METHODS: Ten subjects without known hand pathology and 11 subjects with known stenosing tenosynovitis were assessed on tasks involving hand function at varied speeds, including forceful and gradual making of a fist and the quick and slow grip of a baseball using an animation glove to record range of motion and measures of velocity (CyberGlove II). RESULTS: In normal subjects, peak extension and flexion velocity of the index and middle finger was highest in the metacarpophalangeal and lowest in the distal interphalangeal; however, the converse was true in the ring finger. In those subjects with stenosing tenosynovitis, the animation glove was able to detect a triggering event during assessment. Furthermore, there was a significant decrease in the maximum velocity of the proximal interphalangeal joint observed with the slow fist task in both flexion and extension (55%, P < .01) in the affected hand when compared with the unaffected hand. CONCLUSIONS: The CyberGlove II can be utilized in the dynamic functional analysis of the hand and is able to detect a triggering event in subjects with known stenosing tenosynovitis. Those subjects demonstrate a significant decrease in maximum velocity in slow fist tasks, highlighting the need for comprehensive assessment to ascertain the full extent of functional limitations that can occur in the setting of hand pathology.