Estimating heart rate using wrist-type Photoplethysmography and acceleration sensor while running

Fukushima, Hayato; Kawanaka, Haruki; Bhuiyan, Md. Shoaib; Oguri, Kazuta

doi:10.1109/embc.2012.6346570

Cited by 109 publications

(68 citation statements)

References 2 publications

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“…There are other approaches that try to remove the MAs by using accelerometer signals, which include spectrum subtraction [11], [12], joint sparse spectrum reconstruction [13], singular spectrum analysis [2], [14], [15], adaptive filtering [16] and mixture of algorithms [17]. With the aid of an acceleration signal, strong and continuous MAs can be reduced.…”

Section: Introductionmentioning

confidence: 99%

PARHELIA: Particle Filter-Based Heart Rate Estimation From Photoplethysmographic Signals During Physical Exercise

Fujita

Hiromoto

Satō

2018

IEEE Trans. Biomed. Eng.

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Abstract-The photoplethysmographic (PPG) signal is an important source of information for estimating heart rate (HR). However, the PPG signal could be strongly contaminated by the motion artifact (MA) of the subjects, making HR estimation a particularly difficult problem. In this paper, we propose PARHELIA, a PARticle filter-based algorithm for HEart rate estimation using photopLethysmographIc signAls. The proposed method employs a particle filter, and utilizes the simultaneously recorded acceleration signals from a wrist-type sensor, to keep track of multiple HR candidates. This achieves quick recovery from incorrect HR estimations under the strong influence of the MA. Experimental results for a dataset of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.17 beats per minute (BPM) whereas that of the bestknown conventional method, JOSS, is 1.28 BPM. Furthermore, the estimation time of PARHELIA is 20 times shorter than JOSS.

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Section: Introductionmentioning

confidence: 99%

PARHELIA: Particle Filter-Based Heart Rate Estimation From Photoplethysmographic Signals During Physical Exercise

Fujita

Hiromoto

Satō

2018

IEEE Trans. Biomed. Eng.

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“…(4)(5)(6)(7)(8)(9)(10)(11)(12) In these studies, the signal elements of MAs are removed by a statistical signal processing technique such as independent component analysis (ICA). Once MAs can be removed from a PPG signal, the temporal change in heart rate can be estimated by peak-frequency tracking.…”

Section: Related Workmentioning

confidence: 99%

“…(3) There are some studies focusing on the reduction of the influence of MAs. (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) Many of them, however, implicitly or explicitly assume that exercise intensity is constant or changes slightly. It is, therefore, difficult to simply apply these techniques to heart rate estimation during exercise especially when exercise intensity changes dynamically.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Heart Rate during Exercise from a Photoplethysmographic Signal Considering Exercise Intensity

2016

Sensors and Materials

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Heart rate estimation during exercise with photoplethysmographic (PPG) sensors is a difficult problem because motion artifacts (MAs) contaminate PPG signals. In this paper, we propose a method of accurately estimating heart rate during exercise from a PPG signal by considering exercise intensity. The exercise intensity is estimated from acceleration signals simultaneously recorded with the PPG signal. The proposed method dynamically limits the estimation range of heart rate depending on the exercise intensity, resulting in an accurate heart rate estimation during exercise. From the experimental results, we have confirmed that the proposed method can estimate heart rate more accurately than conventional methods.

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“…Some other researchers compare the performance of led Red, Green, and Blue to detect heartbeat such as Lee et al [11]. Photodetector sensors are used by Fukushima et al [12], Miah et al [13], and Izneid et al [14] who used photoplethysmography theory to detect heart rate.…”

Section: Introductionmentioning

confidence: 99%

Low-cost portable heart rate monitoring based on photoplethysmography and decision tree

Tunggal¹,

Latif²,

Iswanto³

2016

AIP Conference Proceedings

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Abstract. Heart disease is caused by a cardiac function which does not work optimally. This illness can be detected by sensing a pulse that is defined as the rhythm of the heartbeat. Some researchers have conducted a study to determine and monitor heart rate. Researches resulted in very expensive and not portable tools. Therefore, this paper examines portable heart rate monitoring with photoplethysmography method and decision tree algorithm. The photoplethysmography method is an optical technique that is cheap and simple that can be used to detect changes in blood volume in the microvascular network. The method modified on the transmitter and receiver sensors. The decision tree algorithm is used to make decisions for one's pulse form of digital data retrieval heart rate. With the decision tree algorithm obtained a normal heart, Bradycardia, and Tachycardia. The method and algorithm result in a much cheaper tool which can detect heart rate which later is categorized into the normal heart, Bradycardia, or Tachycardia.

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Estimating heart rate using wrist-type Photoplethysmography and acceleration sensor while running

Cited by 109 publications

References 2 publications

PARHELIA: Particle Filter-Based Heart Rate Estimation From Photoplethysmographic Signals During Physical Exercise

PARHELIA: Particle Filter-Based Heart Rate Estimation From Photoplethysmographic Signals During Physical Exercise

Estimation of Heart Rate during Exercise from a Photoplethysmographic Signal Considering Exercise Intensity

Low-cost portable heart rate monitoring based on photoplethysmography and decision tree

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