Author Archives: Daniel Herrera

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Unobtrusive continuous monitoring of important vital signs and activity metrics has the potential to provide remote health monitoring, at-home screening, and rapid notification of critical events such as heart attacks, falls, or respiratory distress. This paper contains validation results of a wireless Bluetooth Low Energy (BLE) patch sensor consisting of two electrocardiography (ECG) electrodes, a microcontroller, a tri-axial accelerometer, and a BLE transceiver. The sensor measures heart rate, heart rate variability (HRV), respiratory rate, posture, steps, and falls and was evaluated on a total of 25 adult participants who performed breathing exercises, activities of daily living (ADLs), various stretches, stationary cycling, walking/running, and simulated falls. Compared to reference devices, the heart rate measurement had a mean absolute error (MAE) of less than 2 bpm, time-domain HRV measurements had an RMS error of less than 15 ms, respiratory rate had an MAE of 1.1 breaths per minute during metronome breathing, posture detection had an accuracy of over 95% in two of the three patch locations, steps were counted with an absolute error of less than 5%, and falls were detected with a sensitivity of 95.2% and specificity of 100%...

Wireless Patch Sensor for Remote Monitoring of Heart Rate Respiration, Activity, and Falls

Unobtrusive continuous monitoring of important vital signs and activity metrics has the potential to provide remote health monitoring, at-home screening, and rapid notification of critical events such as heart attacks, falls, or respiratory distress. This paper contains validation results of a wireless Bluetooth Low Energy (BLE) patch sensor consisting of two electrocardiography (ECG) electrodes, a microcontroller, a tri-axial accelerometer, and a BLE transceiver. The sensor measures heart rate, heart rate variability (HRV), respiratory rate, posture, steps, and falls and was evaluated on a total of 25 adult participants who performed breathing exercises, activities of daily living (ADLs), various stretches, stationary cycling, walking/running, and simulated falls. Compared to reference devices, the heart rate measurement had a mean absolute error (MAE) of less than 2 bpm, time-domain HRV measurements had an RMS error of less than 15 ms, respiratory rate had an MAE of 1.1 breaths per minute during metronome breathing, posture detection had an accuracy of over 95% in two of the three patch locations, steps were counted with an absolute error of less than 5%, and falls were detected with a sensitivity of 95.2% and specificity of 100%...

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Sleep Apnea Syndrome (SAS) is highly prevalent worldwide and severely affects quality of life. SAS screening with self-reported symptoms, upper-airway features, physical exam and questionnaires have been reported with sensitivity but poor specificity. Challenges with polysomnography (PSG) for SAS screening include high operating costs, inadequate availability and limited repeatability. We present a novel SAS screening tool using the Vital Connect HealthPatch™ sensor.

Wireless Patch Sensor for Screening of Sleep Apnea

Sleep Apnea Syndrome (SAS) is highly prevalent worldwide and severely affects quality of life. SAS screening with self-reported symptoms, upper-airway features, physical exam and questionnaires have been reported with sensitivity but poor specificity. Challenges with polysomnography (PSG) for SAS screening include high operating costs, inadequate availability and limited repeatability. We present a novel SAS screening tool using the Vital Connect HealthPatch™ sensor.

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Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatch sensor. Volunteers (n=53, AHI: 0.1?-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150- second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values resulted in linear correlation coefficients of 0.87, 0.88 and 0.92 for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI<15) and moderate-to-severe apnea (AHI>=15) with an accuracy (95% confidence intervals) of 89.4% (77.4?-95.4%), 85.0% (70.9?-92.9%), and 82.9% (67.3?-91.9%) for the three locations, respectively. Overnight physiological monitoring using a wireless patch sensor provides an accurate estimate of AHI...

Automated Prediction of the Apnea-Hypopnea Index using a Wireless Patch Sensor

Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatch sensor. Volunteers (n=53, AHI: 0.1?-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150- second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values resulted in linear correlation coefficients of 0.87, 0.88 and 0.92 for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI<15) and moderate-to-severe apnea (AHI>=15) with an accuracy (95% confidence intervals) of 89.4% (77.4?-95.4%), 85.0% (70.9?-92.9%), and 82.9% (67.3?-91.9%) for the three locations, respectively. Overnight physiological monitoring using a wireless patch sensor provides an accurate estimate of AHI...

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Remote monitoring and wearable technologies could help to effectively manage health, monitor safety andreduce the staggering health care costs. The present studyis designed to investigate the efficacy of disposable wireless HealthPatch sensors for continuous and long-term monitoring in senior subjects over consecutive 50-days in their home setting. Patch sensor is worn on the chest that allows remote and real-time monitoring of vital sign measurements and falls. Patch data and nightly questionnaire responses were obtained in 76 participants (age 59-85 years) over 3603 days using 1333 patches, collectively. The performance of heart rate, respiration rate and skin temperature was assessed on day-1 and day-4 that estimated the mean absolute errors to be <3 beats/min, <3 breaths/min and <1.2 C, respectively compared to their respective reference devices. The vital signs showed no significant differences between start and end of a 3-day wear cycle. False positive rate of fall detection was 0.0027 falls/day. The participants reported the patch wear very/fairly comfortable for 88.2% of days of wear. The wireless patch sensors have demonstrated high performance over its 3-day wear cycle, great compliance, and positive user feedback on wearability and usability. Thus, the patch sensors are very efficient and suitable for remote long-term monitoring at uncontrolled home setting...

Long-term Remote Monitoring of Vital Signs using a Wireless Patch Sensor

Remote monitoring and wearable technologies could help to effectively manage health, monitor safety andreduce the staggering health care costs. The present studyis designed to investigate the efficacy of disposable wireless HealthPatch sensors for continuous and long-term monitoring in senior subjects over consecutive 50-days in their home setting. Patch sensor is worn on the chest that allows remote and real-time monitoring of vital sign measurements and falls. Patch data and nightly questionnaire responses were obtained in 76 participants (age 59-85 years) over 3603 days using 1333 patches, collectively. The performance of heart rate, respiration rate and skin temperature was assessed on day-1 and day-4 that estimated the mean absolute errors to be <3 beats/min, <3 breaths/min and <1.2 C, respectively compared to their respective reference devices. The vital signs showed no significant differences between start and end of a 3-day wear cycle. False positive rate of fall detection was 0.0027 falls/day. The participants reported the patch wear very/fairly comfortable for 88.2% of days of wear. The wireless patch sensors have demonstrated high performance over its 3-day wear cycle, great compliance, and positive user feedback on wearability and usability. Thus, the patch sensors are very efficient and suitable for remote long-term monitoring at uncontrolled home setting...

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This paper describes an adhesive sensor system worn on the skin that automatically detects human falls. The sensor, which consists of a tri-axial accelerometer, a microcontroller and a Bluetooth Low Energy transceiver, can be worn anywhere on a subject’s torso and in any orientation. In order to distinguish easily between falls and activities of daily living (ADL), a possible fall is detected only if an impact is detected and if the subject is horizontal shortly afterwards. As an additional criterion to reduce false positives, a fall is confirmed if the user activity level several seconds after a possible fall is below a threshold. Intentional falls onto a gymnastics mat were performed by 10 volunteers (total of 297 falls); ADL were performed by 15 elderly volunteers (total of 315 ADL). The fall detection algorithm provided a sensitivity of 99% and a specificity of 100%..

Skin-Contact Sensor for Automatic Fall Detection

This paper describes an adhesive sensor system worn on the skin that automatically detects human falls. The sensor, which consists of a tri-axial accelerometer, a microcontroller and a Bluetooth Low Energy transceiver, can be worn anywhere on a subject’s torso and in any orientation. In order to distinguish easily between falls and activities of daily living (ADL), a possible fall is detected only if an impact is detected and if the subject is horizontal shortly afterwards. As an additional criterion to reduce false positives, a fall is confirmed if the user activity level several seconds after a possible fall is below a threshold. Intentional falls onto a gymnastics mat were performed by 10 volunteers (total of 297 falls); ADL were performed by 15 elderly volunteers (total of 315 ADL). The fall detection algorithm provided a sensitivity of 99% and a specificity of 100%..