Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

OBJECTIVE: New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. METHODS: Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. RESULTS: The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. SIGNIFICANCE: The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.

Validation of a Standalone Smartphone Application for Measuring Heart Rate Using Imaging Photoplethysmography.

BACKGROUND: The aim of this study was to determine the accuracy of a freely available smartphone application, Cardiio app (Cardiio, Inc., Cambridge, MA), to measure heart rate from the finger or face using imaging photoplethysmography, by comparing against an FDA-cleared pulse oximeter at rest, and after moderate to vigorous exercise. METHODS: A total of 40 healthy adults participated in this study. Participants engaged in a period of rest, followed by 3 min of moderate to vigorous intensity cycling on a stationary bicycle. Heart rate measurements were obtained from both the finger and face of participants using the Cardiio app at rest, immediately after exercise, 1-2 min after exercise, and 2-3 min after exercise. Concurrent heart rate readings using an FDA-cleared finger pulse oximeter served as the reference measurement. RESULTS: There was a very strong agreement between heart rate measurements obtained using the Cardiio app and the pulse oximeter, both at rest (r = 0.99 for finger, r = 0.97 for face) and after exercise (r = 0.99 for finger, r = 0.97 for face). At rest, the accuracy of the Cardiio app was ±1.58 beats per minute (bpm) (or ±2.27%) using the finger mode and ±2.28 bpm (or ±3.17%) for the face mode, compared to the pulse oximeter. After moderate to vigorous exercise, the accuracy of the Cardiio app was ±2.97 bpm (or ±2.79%) using the finger mode and ±5.31 bpm (or ±4.50%) for the face mode, compared to the pulse oximeter. CONCLUSION: The Cardiio app provided accurate heart rate measurements from the finger and face, both at rest and after exercise.

Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment.

BACKGROUND: Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision-making. There is an unmet need for medical-grade diagnostic wrist-worn devices that provide long-term, continuous AF monitoring. METHODS AND RESULTS: We evaluated the performance of a wrist-worn device with lead-I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography-based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14-day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15-minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%-98.0%) and 98.1% specific (95% CI, 97.2%-99.1%) for interval-level AF detection. Photoplethysmography-derived AF burden estimation was highly correlated with the reference device burden (R2=0.986) with a mean difference of 0.8% (95% limits of agreement, -6.6% to 8.2%). CONCLUSIONS: Continuous monitoring using a photoplethysmography-based convolutional neural network incorporated in a wrist-worn device has clinical-grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist-worn wearables for diagnosis and clinical management of AF. REGISTRATION INFORMATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04546763.

Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor.

The special requirements for a seizure detector suitable for everyday use in terms of cost, comfort, and social acceptance call for alternatives to electroencephalography (EEG)-based methods. Therefore, we developed an algorithm for automatic detection of generalized tonic-clonic (GTC) seizures based on sympathetically mediated electrodermal activity (EDA) and accelerometry measured using a novel wrist-worn biosensor. The problem of GTC seizure detection was posed as a supervised learning task in which the goal was to classify 10-s epochs as a seizure or nonseizure event based on 19 extracted features from EDA and accelerometry recordings using a Support Vector Machine. Performance was evaluated using a double cross-validation method. The new seizure detection algorithm was tested on >4,213 h of recordings from 80 patients and detected 15 (94%) of 16 of the GTC seizures from seven patients with 130 false alarms (0.74 per 24 h). This algorithm can potentially provide a convulsive seizure alarm system for caregivers and objective quantification of seizure frequency.

Diffusion of particles in the extracellular matrix: the effect of repulsive electrostatic interactions.

Diffusive transport of macromolecules and nanoparticles in charged fibrous media is of interest in many biological applications, including drug delivery and separation processes. Experimental findings have shown that diffusion can be significantly hindered by electrostatic interactions between the diffusing particle and charged components of the extracellular matrix. The implications, however, have not been analyzed rigorously. Here, we present a mathematical framework to study the effect of charge on the diffusive transport of macromolecules and nanoparticles in the extracellular matrix of biological tissues. The model takes into account steric, hydrodynamic, and electrostatic interactions. We show that when the fiber size is comparable to the Debye length, electrostatic forces between the fibers and the particles result in slowed diffusion. However, as the fiber diameter increases the repulsive forces become less important. Our results explain the experimental observations that neutral particles diffuse faster than charged particles. Taken together, we conclude that optimal particles for delivery to tumors should be initially cationic to target the tumor vessels and then change to neutral charge after exiting the blood vessels.

Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.

Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.

Figure Correction: Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study.

[This corrects the article DOI: 10.2196/mhealth.7275.].

Diagnostic Accuracy of a Novel Mobile Phone Application for the Detection and Monitoring of Atrial Fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in adults, associated with significant morbidity, increased mortality, and rising health-care costs. Simple and available tools for the accurate detection of arrhythmia recurrence in patients after electrical cardioversion (CV) or ablation procedures for AF can help to guide therapeutic decisions. We conducted a prospective, single-center study to evaluate the accuracy of Cardiio Rhythm Mobile Application (CRMA) for AF detection. Patients >18 years of age who were scheduled for elective CV for AF were enrolled in the study. CRMA finger pulse recordings, utilizing an iPhone camera, were obtained before (pre-CV) and after (post-CV) the CV. The findings were validated against surface electrocardiograms. Ninety-eight patients (75.5% men), mean age of 67.7 ± 10.5 years, were enrolled. No electrocardiogram for validation was available in 1 case. Pre-CV CRMA readings were analyzed in 97 of the 98 patients. Post-CV CRMA readings were analyzed for 92 of 93 patients who underwent CV. One patient left before the recording was obtained. The Cardiio Rhythm Mobile Application correctly identified 94 of 101 AF recordings (93.1%) as AF and 80 of 88 non-AF recordings (90.1%) as non-AF. The sensitivity was 93.1% (95% confidence interval [CI] = 86.9% to 97.2%) and the specificity was 90.9% (95% CI = 82.9% to 96.0%). The positive predictive value was 92.2% (95% CI = 85.8% to 95.8%) and the negative predictive value was 92.0% (95% CI = 94.8% to 95.9%). In conclusion, the CRMA demonstrates promising potential in accurate detection and discrimination of AF from normal sinus rhythm in patients with a history of AF.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.

OBJECTIVE: To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms. METHODS: We trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison. RESULTS: In the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924-0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%). CONCLUSIONS: In this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.

Contact-Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals.

BACKGROUND: We aimed to evaluate a novel method of atrial fibrillation (AF) screening using an iPhone camera to detect and analyze photoplethysmographic signals from the face without physical contact by extracting subtle beat-to-beat variations of skin color that reflect the cardiac pulsatile signal. METHODS AND RESULTS: Patients admitted to the cardiology ward of the hospital for clinical reasons were recruited. Simultaneous facial and fingertip photoplethysmographic measurements were obtained from 217 hospital inpatients (mean age, 70.3±13.9 years; 71.4% men) facing the front camera and with an index finger covering the back camera of 2 independent iPhones before a 12-lead ECG was recorded. Backdrop and background light intensity was monitored during signal acquisition. Three successive 20-second (total, 60 seconds) recordings were acquired per patient and analyzed for heart rate regularity by Cardiio Rhythm (Cardiio Inc, Cambridge, MA) smartphone application. Pulse irregularity in ≥1 photoplethysmographic readings or 3 uninterpretable photoplethysmographic readings were considered a positive AF screening result. AF was present on 12-lead ECG in 34.6% (n=75/217) patients. The Cardiio Rhythm facial photoplethysmographic application demonstrated high sensitivity (95%; 95% confidence interval, 87%-98%) and specificity (96%; 95% confidence interval, 91%-98%) in discriminating AF from sinus rhythm compared with 12-lead ECG. The positive and negative predictive values were 92% (95% confidence interval, 84%-96%) and 97% (95% confidence interval, 93%-99%), respectively. CONCLUSIONS: Detection of a facial photoplethysmographic signal to determine pulse irregularity attributable to AF is feasible. The Cardiio Rhythm smartphone application showed high sensitivity and specificity, with low negative likelihood ratio for AF from facial photoplethysmographic signals. The convenience of a contact-free approach is attractive for community screening and has the potential to be useful for distant AF screening.

Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study.

BACKGROUND: Modern smartphones allow measurement of heart rate (HR) by detecting pulsatile photoplethysmographic (PPG) signals with built-in cameras from the fingertips or the face, without physical contact, by extracting subtle beat-to-beat variations of skin color. OBJECTIVE: The objective of our study was to evaluate the accuracy of HR measurements at rest and after exercise using a smartphone-based PPG detection app. METHODS: A total of 40 healthy participants (20 men; mean age 24.7, SD 5.2 years; von Luschan skin color range 14-27) underwent treadmill exercise using the Bruce protocol. We recorded simultaneous PPG signals for each participant by having them (1) facing the front camera and (2) placing their index fingertip over an iPhone's back camera. We analyzed the PPG signals from the Cardiio-Heart Rate Monitor + 7 Minute Workout (Cardiio) smartphone app for HR measurements compared with a continuous 12-lead electrocardiogram (ECG) as the reference. Recordings of 20 seconds' duration each were acquired at rest, and immediately after moderate- (50%-70% maximum HR) and vigorous- (70%-85% maximum HR) intensity exercise, and repeated successively until return to resting HR. We used Bland-Altman plots to examine agreement between ECG and PPG-estimated HR. The accuracy criterion was root mean square error (RMSE) ≤5 beats/min or ≤10%, whichever was greater, according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation EC-13 standard. RESULTS: We analyzed a total of 631 fingertip and 626 facial PPG measurements. Fingertip PPG-estimated HRs were strongly correlated with resting ECG HR (r=.997, RMSE=1.03 beats/min or 1.40%), postmoderate-intensity exercise (r=.994, RMSE=2.15 beats/min or 2.53%), and postvigorous-intensity exercise HR (r=.995, RMSE=2.01 beats/min or 1.93%). The correlation of facial PPG-estimated HR was stronger with resting ECG HR (r=.997, RMSE=1.02 beats/min or 1.44%) than with postmoderate-intensity exercise (r=.982, RMSE=3.68 beats/min or 4.11%) or with postvigorous-intensity exercise (r=.980, RMSE=3.84 beats/min or 3.73%). Bland-Altman plots showed better agreement between ECG and fingertip PPG-estimated HR than between ECG and facial PPG-estimated HR. CONCLUSIONS: We found that HR detection by the Cardiio smartphone app was accurate at rest and after moderate- and vigorous-intensity exercise in a healthy young adult sample. Contact-free facial PPG detection is more convenient but is less accurate than finger PPG due to body motion after exercise.

Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

BACKGROUND: Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. METHODS AND RESULTS: Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single-lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77-99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51-87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97-99%] versus 99.4% [95% CI 99-100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38-67%] versus 76.9% [95% CI 56-91%]); both had a very high negative predictive value (99.8% [95% CI 99-100%] versus 99.2% [95% CI 98-100%]). CONCLUSIONS: The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population-based screening for AF.

Autonomic changes following generalized tonic clonic seizures: An analysis of adult and pediatric patients with epilepsy.

OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is the most common cause of mortality directly related to epilepsy. Its incidence is higher in adult patients and its pathophysiology remains poorly understood, but likely involves autonomic dysregulation following generalized tonic clonic seizures (GTCS). In the current study, we aimed to analyze post-ictal autonomic changes following GTCS in adult and pediatric patients. METHODS: Patients admitted to the epilepsy monitoring unit were prospectively recruited, and wore an electrodermal activity (EDA) wrist sensor that continuously measured sympathetic activity while being monitored with EEG and EKG electrodes. Peri-ictal EDA parameters were assessed as a measure of sympathetic activity. Peri-ictal parasympathetic activity was determined through the high frequency component (HF) analysis of heart rate variability (HRV). The duration of post-ictal generalized EEG suppression (PGES) was also documented. RESULTS: Twenty patients with GTCS were included in the study on whom 30 GTCS were recorded. PGES duration strongly correlated with age (r=0.62, p=0.004) and measures of the EDA response. After controlling for PGES duration, we found pediatric patients had greater sympathetic activation measured as log rising portion of the area under the curve of the EDA response (ß=+0.67, p=0.034) and a higher degree of vagal suppression measured as maximal percentage change of HF power (ß=-12.65, p=0.0036). CONCLUSION: Sympathetic activity can be measured in the peri-ictal period, and directly correlates with PGES duration. Age is a significant determinant of the sympathetic and parasympathetic response following a GTCS; given the same PGES duration, pediatric patients demonstrate stronger sympathetic activation and higher vagal suppression. However, the increase in PGES duration with age and the associated autonomic dysregulation may provide clues as to why there is a variable vulnerability to SUDEP across age groups.

Advancements in noncontact, multiparameter physiological measurements using a webcam.

We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.

A wearable sensor for unobtrusive, long-term assessment of electrodermal activity.

Electrodermal activity (EDA) is a sensitive index of sympathetic nervous system activity. Due to the lack of sensors that can be worn comfortably during normal daily activity and over extensive periods of time, research in this area is limited to laboratory settings or artificial clinical environments. We developed a novel, unobtrusive, nonstigmatizing, wrist-worn integrated sensor, and present, for the very first time, a demonstration of long-term, continuous assessment of EDA outside of a laboratory setting. We evaluated the performance of our device against a Food and Drug Administration (FDA) approved system for the measurement of EDA during physical, cognitive, as well as emotional stressors at both palmar and distal forearm sites, and found high correlations across all the tests. We also evaluated the choice of electrode material by comparing conductive fabric with Ag/AgCl electrodes and discuss the limitations found. An important result presented in this paper is evidence that the distal forearm is a viable alternative to the traditional palmar sites for EDA measurements. Our device offers the unprecedented ability to perform comfortable, long-term, and in situ assessment of EDA. This paper opens up opportunities for future investigations that were previously not feasible, and could have far-reaching implications for diagnosis and understanding of psychological or neurological conditions.

Motion-tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography.

This paper addresses the design considerations and critical evaluation of a novel embodiment for wearable photoplethysmography (PPG) comprising a magnetic earring sensor and wireless earpiece. The miniaturized sensor can be worn comfortably on the earlobe and contains an embedded accelerometer to provide motion reference for adaptive noise cancellation. The compact wireless earpiece provides analog signal conditioning and acts as a data-forwarding device via a radio frequency transceiver. Using Bland-Altman and correlation analysis, we evaluated the performance of the proposed system against an FDA-approved ECG measurement device during daily activities. The mean +/- standard deviation (SD) of the differences between heart rate measurements from the proposed device and ECG (expressed as percentage of the average between the two techniques) along with the 95% limits of agreement (LOA = +/-1.96 SD) was 0.62% +/- 4.51% (LOA = -8.23% and 9.46%), -0.49% +/- 8.65% (-17.39% and 16.42%), and -0.32% +/- 10.63% (-21.15% and 20.52%) during standing, walking, and running, respectively. Linear regression indicated a high correlation between the two measurements across the three evaluated conditions (r = 0.97, 0.82, and 0.76, respectively with p < 0.001). The new earring PPG system provides a platform for comfortable, robust, unobtrusive, and discreet monitoring of cardiovascular function.

Wearable sensors: opportunities and challenges for low-cost health care.

Wearable sensors enable long-term continuous physiological monitoring, which is important for the treatment and management of many chronic illnesses, neurological disorders, and mental health issues. Examples include: diabetes, autism spectrum disorder (ASD), depression, drug addition, and anxiety disorders. In this paper, we present a few mobile health technologies developed by our group and also discuss emerging opportunities as well as existing challenges. Technologies presented include wearable sensors for electrodermal activity (EDA) and mobile plethysmography as well as mobile phones and the supporting wireless network architecture.

Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor.

We present a novel method for monitoring sympathetic nervous system activity during epileptic seizures using a wearable sensor measuring electrodermal activity (EDA). The wearable sensor enables long-term, continuous EDA recordings from patients. Preliminary results from our pilot study suggest that epileptic seizures induce a surge in EDA. These changes are greater in generalized tonic-clonic seizures and reflect a massive sympathetic discharge. This paper offers a new approach for investigating the relationship between epileptic seizures and autonomic alterations.

iCalm: wearable sensor and network architecture for wirelessly communicating and logging autonomic activity.

Widespread use of affective sensing in healthcare applications has been limited due to several practical factors, such as lack of comfortable wearable sensors, lack of wireless standards, and lack of low-power affordable hardware. In this paper, we present a new low-cost, low-power wireless sensor platform implemented using the IEEE 802.15.4 wireless standard, and describe the design of compact wearable sensors for long-term measurement of electrodermal activity, temperature, motor activity, and photoplethysmography. We also illustrate the use of this new technology for continuous long-term monitoring of autonomic nervous system and motion data from active infants, children, and adults. We describe several new applications enabled by this system, discuss two specific wearable designs for the wrist and foot, and present sample data.