Wireless patch sensor for remote monitoring of heart rate, respiration, activity, and falls

Chan, Alexander M.; Selvaraj, Nandakumar; Ferdosi, Nima; Narasimhan, R.

doi:10.1109/embc.2013.6610948

Cited by 86 publications

(77 citation statements)

References 3 publications

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“…It would be also difficult to ensure that the device is placed at the same position during the different experiments. In order to overcome this issue in future work, we envisage that devices that can be attached to the skin as patches will be commonly available to allow continuous and unobtrusive monitoring of gait [38]- [41]. In addition, we plan to investigate the use of the proposed technique on an ear-worn sensor.…”

Section: Resultsmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

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Abstract-A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can be applied only to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The user's gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real-time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained ∼ 84% accuracy in the recognition of abnormal gait segments lasting ∼ 5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.

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Section: Resultsmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

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“…The population was selected to be consistent with previous device performance validation studies [20] (K152139). Forty-four subjects were recruited with 30 participants enrolled: 77% male, 23% female, mean age of 35.9 ± 10.1 years (range 21–52), and a mean BMI of 28.1 ± 3.6 (range 20.1–34.1).…”

Section: Resultsmentioning

confidence: 99%

“…Comparator devices were selected to reproduce the identical validation previously published by the predicate device [20]. …”

Section: Methodsmentioning

confidence: 99%

A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments

Sen-Gupta¹,

Wright²,

Caccese³

et al. 2019

Digit Biomark

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Background: Increasingly, drug and device clinical trials are tracking activity levels and other quality of life indices as endpoints for therapeutic efficacy. Trials have traditionally required intermittent subject visits to the clinic that are artificial, activity-intensive, and infrequent, making trend and event detection between visits difficult. Thus, there is an unmet need for wearable sensors that produce clinical quality and medical grade physiological data from subjects in the home. The current study was designed to validate the BioStamp nPoint® system (MC10 Inc., Lexington, MA, USA), a new technology designed to meet this need. Objective: To evaluate the accuracy, performance, and ease of use of an end-to-end system called the BioStamp nPoint. The system consists of an investigator portal for design of trials and data review, conformal, low-profile, wearable biosensors that adhere to the skin, a companion technology for wireless data transfer to a proprietary cloud, and algorithms for analyzing physiological, biometric, and contextual data for clinical research. Methods: A prospective, nonrandomized clinical trial was conducted on 30 healthy adult volunteers over the course of two continuous days and nights. Supervised and unsupervised study activities enabled performance validation in clinical and remote (simulated “at home”) environments. System outputs for heart rate (HR), heart rate variability (HRV) (including root mean square of successive differences [RMSSD] and low frequency/high frequency ratio), activity classification during prescribed activities (lying, sitting, standing, walking, stationary biking, and sleep), step count during walking, posture characterization, and sleep metrics including onset/wake times, sleep duration, and respiration rate (RR) during sleep were evaluated. Outputs were compared to FDA-cleared comparator devices for HR, HRV, and RR and to ground truth investigator observations for activity and posture classifications, step count, and sleep events. Results: Thirty participants (77% male, 23% female; mean age 35.9 ± 10.1 years; mean BMI 28.1 ± 3.6) were enrolled in the study. The BioStamp nPoint system accurately measured HR and HRV (correlations: HR = 0.957, HRV RMSSD = 0.965, HRV ratio = 0.861) when compared to Actiheart^TM. The system accurately monitored RR (mean absolute error [MAE] = 1.3 breaths/min) during sleep when compared to a Capnostream35^TM end-tidal CO₂ monitor. When compared with investigator observations, the system correctly classified activities and posture (agreement = 98.7 and 92.9%, respectively), step count (MAE = 14.7, < 3% of actual steps during a 6-min walk), and sleep events (MAE: sleep onset = 6.8 min, wake = 11.5 min, sleep duration = 13.7 min) with high accuracy. Participants indicated “good” to “excellent” usability (average System Usability Scale score of 81.3) and preferred the BioStamp nPoint system over both the Actiheart (86%) and Capnostream (97%) devices. Conclusions: The present study validated the BioSt...

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“…Several papers published by the manufacturer showed that the wireless patch records data in a robust and reliable way [19, 20]. The data recorded by the wireless patch were directly and continuously transferred to a connected iPod for the entire duration of the recording.…”

Section: Methodsmentioning

confidence: 99%

A Pilot Study Evaluating the Physiological Parameters of Performance-Induced Stress in Undergraduate Music Students

Fenema¹,

Gal²,

Griend³

et al. 2018

Digit Biomark

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Music performance anxiety (MPA) is a specific condition for musicians. Although it can have a negative influence on their music careers, little attention is paid to this phenomenon both in the professional environment and in stress research. In the current pilot study, insight was gained into the physiology of the autonomic stress response related to anxiety in musicians when performing on stage by using a wearable biosensor patch for registration of a range of physiological parameters. Also, the validity of two different psychometric questionnaires in objectifying the stress response on stage to predict the individual stress response was explored. The autonomic physiological parameters (heart rate, respiratory rate, skin temperature) of 11 violists and violinists were collected while performing on stage and in resting state using the VitalConnect HealthPatch®. In addition, scores on validated questionnaires in research on MPA (State Anxiety Inventory, Kenny Music Performance Anxiety Inventory, Short Form Health Survey) were collected in order to try to objectify the magnitude of the subjective level of both MPA and experienced stress. The registration of the autonomic parameters showed a significant increase in heart rate, respiratory rate, and stress level from resting state measurements during stage performance. Analysis of heart rate variability showed a shift from indices of parasympathetic nervous system activity during baseline measurements towards indices of sympathetic nervous system activity during stress measurements. Surprisingly, none of the questionnaires was correlated to the physiological stress parameters on stage. In conclusion, the wearable biosensor patch proved to be an adequate tool to assess physiological stress parameters on stage. The different questionnaires did not contribute to the prediction of its occurrence in a group of musicians.

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Wireless patch sensor for remote monitoring of heart rate, respiration, activity, and falls

Cited by 86 publications

References 3 publications

An On-Node Processing Approach for Anomaly Detection in Gait

An On-Node Processing Approach for Anomaly Detection in Gait

A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments

A Pilot Study Evaluating the Physiological Parameters of Performance-Induced Stress in Undergraduate Music Students

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