Monitoring of Respiration and Heartbeat during Sleep using a Flexible Piezoelectric Film Sensor and Empirical Mode Decomposition

Bu, Nan; Ueno, Naohiro; Fukuda, Osamu

doi:10.1109/iembs.2007.4352551

Cited by 71 publications

(40 citation statements)

References 13 publications

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“…In order to decide the noise level and IMFs corresponding to heartbeat, we compute the power spectral density of each IMF using Welch's method. In our algorithm, the IMFs with peak frequency, F i , in range of 0.8-3.0 Hz are determined as a component of heartbeat signal [9]. [10] validates these cutoff limits on the output of sensors in a designed Heartphone.…”

Section: ) Test If D(t) Becomes a Zero-mean Signal Then D(t) Ismentioning

confidence: 58%

Multi-channel heart-beat detection

Norouzi

Aarabi

2013

2013 IEEE Global Conference on Signal and Information Processing

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In this paper, we propose a heart-rate measurement system employing triple sensing mechanisms (face color changes, finger color changes, and heart sound measurment) on an iPhone. The three proposed measurement systems each provide an independent heart-rate estimate as well as a combined heart-rate estimate based on the fusion of the individual sensors. Obtaining a single representation of the cardiac cycle from these three modalities requires data-level fusion. In the proposed method, we introduce a new heart-rate measurement enhancement method based on the Empirical Mode Decomposition (EMD). To measure the performance of the proposed method, we collected data on 56 subjects. Our experimental results show that the Root Mean Square Error (as compared to the result from a pulse oximeter) from our proposed method is 3.6 which provides better performance than traditional filtering in terms of noise reduction. Furthermore, this fused result is more accurate than any single modality.

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Section: ) Test If D(t) Becomes a Zero-mean Signal Then D(t) Ismentioning

confidence: 58%

Multi-channel heart-beat detection

Norouzi

Aarabi

2013

2013 IEEE Global Conference on Signal and Information Processing

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“…Implicit sensors other than chairs that focus on vital sign detection include respiration and heart rate in beds [3,11], blood pressure in toilet seats [24], and heart rate in car seats [7]. Ford has recently been developing heart rate sensors embedded in the backrest to measure heart rate through certain clothing materials.…”

Section: Related Workmentioning

confidence: 99%

Health chair

Griffiths

Saponas

Brush

2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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People interact with chairs frequently, making them a potential location to perform implicit health sensing that requires no additional effort by users. We surveyed 550 participants to understand how people sit in chairs and inform the design of a chair that detects heart and respiratory rate from the armrests and backrests of the chair respectively. In a laboratory study with 18 participants, we evaluated a range of common sitting positions to determine when heart rate and respiratory rate detection was possible (32% of the time for heart rate, 52% for respiratory rate) and evaluate the accuracy of the detected rate (83% for heart rate, 73% for respiratory rate). We discuss the challenges of moving this sensing to the wild by evaluating an in-situ study totaling 40 hours with 11 participants. We show that, as an implicit sensor, the chair can collect vital signs data from its occupant through natural interaction with the chair.

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“…Furthermore, we could also select a resonance frequency (9) of the transfer function around the components of the heartbeat, so that output voltages and include the enhanced heartbeat signals. For the low-frequency range where , from (2), (6), (7), and (8) in the time domain, we obtained the following approximations: Similarly, output voltages and due to shifting of the body to the left or right side and heartbeat and respiration, can be obtained as follows: (11) Thus, (12) can be derived (12) When body movements occur, because and (12) can simply be rewritten as (13) where each is linearly proportional to the shift in the CG at the foot to the head side and at the left to the right side, from which we can estimate the movement of the person on the bed.…”

Section: Measurement Of Heartbeat Respiration and Body Movementmentioning

confidence: 99%

Noninvasive Bed Sensing of Human Biosignals Via Piezoceramic Devices Sandwiched Between the Floor and Bed

et al. 2012

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This paper describes a novel bed sensing method for noninvasive, constraint-free, subliminal detection of biosignals. The sensor system detects the heartbeat, respiration, body movement, position change, and scratching motion of a person lying or sleeping on the bed. These biosignals provide not only basic medical information but also sophisticated details about sleep conditions. Thus, the bed sensing method can be used to monitor the health condition of people sleeping at home, as well as that of patients in the hospital. Furthermore, the bed sensor system can detect emerging changes in the physical condition of a person, whether at home or in the hospital. The basic device used for sensing is piezoceramic bonded to stainless steel plate sandwiched between the floor and the four corners of the bed. Thus, no special bed is required. The device, which detects the biosignals generated as mechanical vibrations, has a wide dynamic range and high signal-to-noise (SN) ratio enabling the detection of microvibrations from the heartbeat by the change in acting force, without saturation from body movements. It accurately detects the person's heartbeat and respiration as well as body movement and even the number of scratching motions. The device is suitable for various health monitoring applications including sleep and medical monitoring for circulatory system disorders, as well as diseases characterized by itching.

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Monitoring of Respiration and Heartbeat during Sleep using a Flexible Piezoelectric Film Sensor and Empirical Mode Decomposition

Cited by 71 publications

References 13 publications

Multi-channel heart-beat detection

Multi-channel heart-beat detection

Health chair

Noninvasive Bed Sensing of Human Biosignals Via Piezoceramic Devices Sandwiched Between the Floor and Bed

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