An official website of the United States government
The objective of this study was to investigate the accuracy of a wearable photoplethysmography (PPG) sensor in monitoring heart rate (HR) of sheep housed in high-temperature environments. We hypothesized that the PPG sensor would be capable of differentiating low, normal, and high HR, but would struggle to produce exact HR estimates. The sensor was open source and comprised of a microprocessor (SparkFun®ThingPlus), a photoplethysmography sensor (SparkFun® MAX30101 & MAX32664), and a data storage module (SD Card 16GB), all sewn into a nylon collar with hook-and-loop closure. Sheep (n=4) were divided into 2 groups and exposed to different thermal environments in a cross-over design. The collar was placed around the neck of the sheep during the data collection phase and the manual HR were collected twice a day using a stethoscope. Precision and accuracy of numeric heart rate estimates were analyzed in R software using Pearson correlation and root mean squared prediction errors. Random forest regression was used to classify HR based on low, medium, and high to determine opportunities to leverage the PPG sensors for HR classification. Sensitivity, specificity, and accuracy were measured to evaluate the classification approach. Our results indicated that the PPG-based sensor measured sheep HR with poor accuracy and with higher average estimates in comparison with manually measured with a stethoscope. Categorical classification of HR was also poor, with accuracies ranging from 32% to 49%. Additional work is needed focusing on data analytics, and signal optimization to further rely on PPG sensors for accurately measuring HR in sheep.
more »
« less
Brief research report: Photoplethysmography pulse sensors designed to detect human heart rates are ineffective at measuring horse heart rates
This study sought to evaluate the accuracy of a PPG (photoplethysmography) sensor designed to measure human heart rates in monitoring the distal limb pulse of healthy adult horses. We hypothesized that the PPG sensor is sensitive to placement location and orientation, and that measurement accuracies depend on placement and orientation on the limb. To evaluate this hypothesis, a completely randomized block design with a factorial treatment structure was used. Horses were considered as the block. Limb type (right front, left front, right hind, and left hind) and position of sensor (medial or lateral) were treatments, with levels arranged in a complete (4x2) factorial design. Data were collected by placing the PPG sensor on the limb of each horse (n= 6), with placement location according to the treatment (limb type and location) combination, and taking pulse readings for 60 seconds. Manual heart rates were collected concurrently using a stethoscope. Data were analyzed by calculating root mean square errors (RMSE) for the PPG measurements with the manual heart rates as a gold standard. Variation in RMSE associated with limb and location of sensor were evaluated using a general linear model with fixed effects for limb and location and a random effect for horse. Our results indicated that the PPG sensor was ineffective at measuring horse heart rates, and that the device was insensitive to placement location and orientation. Future work should focus on developing alternative analytics to interpret the data from PPG sensors to better reflect horse heart rates.
more »
« less
- Award ID(s):
- 2106987
- PAR ID:
- 10469215
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Animal Science
- Volume:
- 4
- ISSN:
- 2673-6225
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Camera-based heart rate measurement is becoming an attractive option as a non-contact modality for continuous remote health and engagement monitoring. However, reliable heart rate extraction from camera-based measurement is challenging in realistic scenarios, especially when the subject is moving. In this work, we develop a motion-robust algorithm, labeled RobustPPG, for extracting photoplethysmography signals (PPG) from face video and estimating the heart rate. Our key innovation is to explicitly model and generate motion distortions due to the movements of the person’s face. We use inverse rendering to obtain the 3D shape and albedo of the face and environment lighting from video frames and then render the human face for each frame. The rendered face is similar to the original face but does not contain the heart rate signal; facial movements alone cause pixel intensity variation in the generated video frames. Finally, we use the generated motion distortion to filter the motion-induced measurements. We demonstrate that our approach performs better than the state-of-the-art methods in extracting a clean blood volume signal with over 2 dB signal quality improvement and 30% improvement in RMSE of estimated heart rate in intense motion scenarios.more » « less
-
Abstract This work details the partially observable markov decision process (POMDP) and the point-based value iteration (PBVI) algorithms for use in multisensor systems, specifically, a sensor system capable of heart rate (HR) estimation through wearable photoplethysmography (PPG) and accelerometer signals. PPG sensors are highly susceptible to motion artifact (MA); however, current methods focus more on overall MA filters, rather than action specific filtering. An end-to-end embedded human activity recognition (HAR) System is developed to represent the observation uncertainty, and two action specific PPG MA reducing filters are proposed as actions. PBVI allows optimal action decision-making based on an uncertain observation, effectively balancing correct action choice and sensor system cost. Two central systems are proposed to accompany these algorithms, one for unlimited observation access and one for limited observation access. Through simulation, it can be shown that the limited observation system performs optimally when sensor cost is negligible, while limited observation access performs optimally when a negative reward for sensor use is considered. The final general framework for POMDP and PBVI was applied to a specific HR estimation example. This work can be expanded on and used as a basis for future work on similar multisensor system.more » « less
-
null (Ed.)Abstract Handedness has been associated with behavioral asymmetries between limbs that suggest specialized function of dominant and non-dominant hand. Whether patterns of muscle co-activation, representing muscle synergies, also differ between the limbs remains an open question. Previous investigations of proximal upper limb muscle synergies have reported little evidence of limb asymmetry; however, whether the same is true of the distal upper limb and hand remains unknown. This study compared forearm and hand muscle synergies between the dominant and non-dominant limb of left-handed and right-handed participants. Participants formed their hands into the postures of the American Sign Language (ASL) alphabet, while EMG was recorded from hand and forearm muscles. Muscle synergies were extracted for each limb individually by applying non-negative-matrix-factorization (NMF). Extracted synergies were compared between limbs for each individual, and between individuals to assess within and across participant differences. Results indicate no difference between the limbs for individuals, but differences in limb synergies at the population level. Left limb synergies were found to be more similar than right limb synergies across left- and right-handed individuals. Synergies of the left hand of left dominant individuals were found to have greater population level similarity than the other limbs tested. Results are interpreted with respect to known differences in the neuroanatomy and neurophysiology of proximal and distal upper limb motor control. Implications for skill training in sports requiring dexterous control of the hand are discussed.more » « less
-
Respiration rate and heart rate variability (HRV) due to respiratory sinus arrhythmia (RSA) are physiological measurements that can offer useful diagnostics for a variety of medical conditions. This study uses a wrist-worn wearable development platform from Maxim Integrated and Doppler radar sensor developed by Adnoviv, Inc. to non-invasively measure these physiological signals. Six datasets are recorded comprising of five different individuals in varying physical environments breathing at different respiration rates. First, respiration rates are extracted from photoplethysmography (PPG) and accelerometer data and compared to Doppler radar. The average maximum and minimum difference between Doppler radar extracted RR and PPG, HRV RSA, and accelerometer extracted RR is 0.342 b/m and 0.171 b/m, respectively. Then, waveforms for Doppler radar, PPG, and HRV RSA signals are plotted in time domain and an analysis discusses the physical phenomena associated with the phase alignment of the signals.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fanim.2023.1103812
