Effectiveness of the heartbeat interval error and compensation method on heart rate variability analysis.

The purpose of this study is to evaluate the effectiveness of heartbeat error and compensation methods on heart rate variability (HRV) with mobile and wearable sensor devices. The HRV analysis extracts multiple indices related to the heart and autonomic nervous system from beat-to-beat intervals. These HRV analysis indices are affected by the heartbeat interval mismatch, which is caused by sampling error from measurement hardware and inherent errors from the state of human body. Although the sampling rate reduction is a common method to reduce power consumption on wearable devices, it degrades the accuracy of the heartbeat interval. Furthermore, wearable devices often use photoplethysmography (PPG) instead of electrocardiogram (ECG) to measure heart rate. However, there are inherent errors between PPG and ECG, because the PPG is affected by blood pressure fluctuations, vascular stiffness, and body movements. This paper evaluates the impact of these errors on HRV analysis using dataset including both ECG and PPG from 28 subjects. The evaluation results showed that the error compensation method improved the accuracy of HRV analysis in time domain, frequency domain and non-linear analysis. Furthermore, the error compensation by the algorithm was found to be effective for both PPG and ECG.

Low-Noise Photoplethysmography Sensor Using Correlated Double Sampling for Heartbeat Interval Acquisition.

This study designs a low-power photoplethysmography (PPG) sensor based on the error compensation method for heartbeat interval acquisition. To perform heartbeat monitoring in daily life, it is necessary to obtain long-term and accurate heartbeat interval data with low power consumption, because of the limited size and battery capacity of the PPG sensor. Effective reduction in the power consumption of the sensor requires the duty-cycled LEDs and lowering pulse repetition frequency (PRF), i.e., decreasing the sampling rate. However, these methods reduce the accuracy of the heartbeat interval measurement because of signal-to-noise ratio (SNR) degradation and sampling errors. We propose an algorithm for heartbeat interval error compensation and incorporate a low-noise readout circuit to improve SNR. The readout circuit uses current integration to achieve low duty-cycle LED driving. A correlated double sampling (CDS) is introduced to minimize the random noise arising from the switching operation of the integration circuit. An error compensation method based on the PPG waveform similarity is also introduced using the autocorrelation and linear interpolation. The measurement results obtained from nine subjects show that a total current consumption of 28.2 µA is achieved with a 20-Hz PRF and 0.3% LED duty cycle. The proposed design effectively reduces the mean absolute error (MAE) of the heartbeat interval to an average of 6.2 ms.

A 5-ms Error, 22-µA Photoplethysmography Sensor using Current Integration Circuit and Correlated Double Sampling.

This paper presents a low-power Photoplethysmography (PPG) sensing method. The PPG is commonly used in recent wearable devices to detect cardiovascular information including heartbeat. The heartbeat is useful for physical activity and stress monitoring. However, the PPG circuit consumes large power because it consists of LED and photodiode. To reduce its power consumption without accuracy degradation, a cooperative design of circuits and algorithms is proposed in this work. A straightforward way to reduce the power is intermittent driving of LED, but there is a disadvantage that the signal is contaminated by a noise while circuit switching. To overcome this problem, we introduce correlated double sampling (CDS) method, which samples an integration circuit output twice with short intervals after the LED turns on and uses the difference of these voltage. Furthermore, an up-conversion method using linear interpolation, and an error correction using autocorrelation are introduced. The proposed PPG sensor, which consists of the LED, the photodiode, the current integration circuit, a CMOS switch, an A/D converter, and an MCU, is prototyped. It is evaluated by actual measurement with 22-year-old subject. The measurement results show that 22-µA total current consumption is achieved with 5-ms mean absolute error.

Estimating metabolic equivalents for activities in daily life using acceleration and heart rate in wearable devices.

BACKGROUND: Herein, an algorithm that can be used in wearable health monitoring devices to estimate metabolic equivalents (METs) based on physical activity intensity data, particularly for certain activities in daily life that make MET estimation difficult. RESULTS: Energy expenditure data were obtained from 42 volunteers using indirect calorimetry, triaxial accelerations and heart rates. The proposed algorithm used the percentage of heart rate reserve (%HRR) and the acceleration signal from the wearable device to divide the data into a middle-intensity group and a high-intensity group (HIG). The two groups were defined in terms of estimated METs. Evaluation results revealed that the classification accuracy for both groups was higher than 91%. To further facilitate MET estimation, five multiple-regression models using different features were evaluated via leave-one-out cross-validation. Using this approach, all models showed significant improvements in mean absolute percentage error (MAPE) of METs in the HIG, which included stair ascent, and the maximum reduction in MAPE for HIG was 24% compared to the previous model (HJA-750), which demonstrated a 70.7% improvement ratio. The most suitable model for our purpose that utilized heart rate and filtered synthetic acceleration was selected and its estimation error trend was confirmed. CONCLUSION: For HIG, the MAPE recalculated by the most suitable model was 10.5%. The improvement ratio was 71.6% as compared to the previous model (HJA-750C). This result was almost identical to that obtained from leave-one-out cross-validation. This proposed algorithm revealed an improvement in estimation accuracy for activities in daily life; in particular, the results included estimated values associated with stair ascent, which has been a difficult activity to evaluate so far.

A swallowable sensing device platform with wireless power feeding and chemical reaction actuator.

This paper presents a swallowable sensor device that can be ingested orally, later passing to the stomach, where the device can indwell for long periods. Using wireless communication, it can be egested at any time after it is triggered. This device can indwell using a silicone balloon in the gastrointestinal tract. A chemical reaction inflates the balloon inside the stomach. Then it is deflated to egest the sensor device using an actuator with electrolysis of water. Energy for the actuator with electrolysis can be fed wirelessly. Near field communication and a flexible antenna are used for power feeding and wireless data communication. Because of the flexible balloon and the flexible antenna, the device size can be minimized without performance degradation.

Swallowable sensing device for long-term gastrointestinal tract monitoring.

This paper presents a swallowable sensor device that can be ingested orally, later arriving to the stomach, where the device can indwell for a long term and can be egested at any time after it is triggered using wireless communication. This device can inflate a silicone balloon in the gastrointestinal tract using a chemical reaction. The balloon can be deflated later using electrolysis of water at the time of egestion. A motorless chemical-reaction-based egestion method is proposed to minimize the sensor device size. This device can achieve long-term monitoring in the gastrointestinal tract.

Low-power metabolic equivalents estimation algorithm using adaptive acceleration sampling.

This paper describes a proposed low-power metabolic equivalent estimation algorithm that can calculate the value of metabolic equivalents (METs) from triaxial acceleration at an adaptively changeable sampling rate. This algorithm uses four rates of 32, 16, 8 and 4 Hz. The mode of switching them is decided from synthetic acceleration. Applying this proposed algorithm to acceleration measured for 1 day, we achieved the low root mean squared error (RMSE) of calculated METs, with current consumption that was 41.5 % of the value at 32 Hz, and 75.4 % of the value at 16 Hz.

Normally Off ECG SoC With Non-Volatile MCU and Noise Tolerant Heartbeat Detector.

This paper describes an electrocardiograph (ECG) monitoring SoC using a non-volatile MCU (NVMCU) and a noise-tolerant instantaneous heartbeat detector. The novelty of this work is the combination of the non-volatile MCU for normally off computing and a noise-tolerant-QRS (heartbeat) detector to achieve both low-power and noise tolerance. To minimize the stand-by current of MCU, a non-volatile flip-flop and a 6T-4C NVRAM are used. Proposed plate-line charge-share and bit-line non-precharge techniques also contribute to mitigate the active power overhead of 6T-4C NVRAM. The proposed accurate heartbeat detector uses coarse-fine autocorrelation and a template matching technique. Accurate heartbeat detection also contributes system-level power reduction because the active ratio of ADC and digital block can be reduced using heartbeat prediction. Measurement results show that the fully integrated ECG-SoC consumes 6.14 µ A including 1.28- µA non-volatile MCU and 0.7- µA heartbeat detector.

A Wearable Healthcare System With a 13.7 µA Noise Tolerant ECG Processor.

To prevent lifestyle diseases, wearable bio-signal monitoring systems for daily life monitoring have attracted attention. Wearable systems have strict size and weight constraints, which impose significant limitations of the battery capacity and the signal-to-noise ratio of bio-signals. This report describes an electrocardiograph (ECG) processor for use with a wearable healthcare system. It comprises an analog front end, a 12-bit ADC, a robust Instantaneous Heart Rate (IHR) monitor, a 32-bit Cortex-M0 core, and 64 Kbyte Ferroelectric Random Access Memory (FeRAM). The IHR monitor uses a short-term autocorrelation (STAC) algorithm to improve the heart-rate detection accuracy despite its use in noisy conditions. The ECG processor chip consumes 13.7 µA for heart rate logging application.

Physical activity group classification algorithm using triaxial acceleration and heart rate.

As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.

Large displacement haptic stimulus actuator using piezoelectric pump for wearable devices.

Recently, given Japan's aging society background, wearable healthcare devices have increasingly attracted attention. Many devices have been developed, but most devices have only a sensing function. To expand the application area of wearable healthcare devices, an interactive communication function with the human body is required using an actuator. For example, a device must be useful for medication assistance, predictive alerts of a disease such as arrhythmia, and exercise. In this work, a haptic stimulus actuator using a piezoelectric pump is proposed to realize a large displacement in wearable devices. The proposed actuator drives tactile sensation of the human body. The measurement results obtained using a sensory examination demonstrate that the proposed actuator can generate sufficient stimuli even if adhered to the chest, which has fewer tactile receptors than either the fingertip or wrist.

A more acceptable endoluminal implantation for remotely monitoring ingestible sensors anchored to the stomach wall.

Several types of implant devices have been proposed and introduced into healthcare and telemedicine systems for monitoring physiological parameters, sometimes for very long periods of time. To our disappointment, most of the devices are implanted invasively and by surgery. We often have to surgically remove such devices after they have finished their mission or before the battery becomes worn out. Wearable devices have the possibility to become new modalities for monitoring vital parameters less-invasively. However, for round-the-clock monitoring of data from sensors over long periods of time, it would be better to put them inside the body to avoid causing inconvenience to patients in their daily lives. This study tested a less invasive endoluminal approach and innovative tools (developed during our research into therapeutic capsule endoscopy) for remotely anchoring ingestible sensors to the stomach wall. Preliminary investigations are also described about wireless communication (NFC, ZigBee, and Bluetooth) for low power consumption and inductive extracorporeal power feeding wirelessly to the circuits in a phantom lined with swine gastric mucosa. Electrocardiogram and pH were monitored and those parameters were successfully transmitted by wireless communication ICs to the Internet via a portable device.

Non-contact and noise tolerant heart rate monitoring using microwave doppler sensor and range imagery.

This paper describes a non-contact and noise-tolerant heart beat monitoring system. The proposed system comprises a microwave Doppler sensor and range imagery using Microsoft Kinect™. The possible application of the proposed system is a driver health monitoring. We introduce the sensor fusion approach to minimize the heart beat detection error. The proposed algorithm can subtract a body motion artifact from Doppler sensor output using time-frequency analysis. The body motion artifact is a crucially important problem for biosignal monitoring using microwave Doppler sensor. The body motion speed is obtainable from range imagery, which has 5-mm resolution at 30-cm distance. Measurement results show that the success rate of the heart beat detection is improved about 75% on average when the Doppler wave is degraded by the body motion artifact.

Noise tolerant QRS detection using template matching with short-term autocorrelation.

This paper describes a robust method for heart beat detection from noisy electrocardiogram (ECG) signals. Generally, the QRS-complex of heart beat is extracted from the ECG using a threshold. However, in a noisy condition such a mobile and wearable bio-signal monitoring system, noise increases the incidence of misdetection and false detection of QRS-complex. To prevent incorrect detection, we introduce a novel template matching algorithm. The template waveform can be generated autonomously using a short-term autocorrelation method, which leverages the similarity of QRS-complex waveforms. Simulation results show the proposed method achieves state-of-the-art noise tolerance of heart beat detection.

Noise-tolerant instantaneous heart rate and R-peak detection using short-term autocorrelation for wearable healthcare systems.

This paper describes a robust method of Instantaneous Heart Rate (IHR) and R-peak detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the R-wave interval. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable bio-signal monitoring systems, noise increases the incidence of misdetection and false detection of R-peaks. To prevent incorrect detection, we introduce a short-term autocorrelation (STAC) technique and a small-window autocorrelation (SWAC) technique, which leverages the similarity of QRS complex waveforms. Simulation results show that the proposed method improves the noise tolerance of R-peak detection.

Instantaneous Heart Rate detection using short-time autocorrelation for wearable healthcare systems.

This report describes a robust method of Instantaneous Heart Rate (IHR) detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the interval of R-waves. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable biosignal monitoring systems, various noises (e.g. muscle artifacts from myoelectric signals, electrode motion artifacts) increase incidences of misdetection and false detection because the power consumption and electrode distance of the wearable sensor are limited to reduce its size and weight. To prevent incorrect detection, we use a short-time autocorrelation technique. The proposed method uses similarity of the waveform of the QRS complex. Therefore, it has no threshold calculation Process and it is robust for noisy environment. Simulation results show that the proposed method improves the success rate of IHR detection by up to 37%.