Heart Rate Monitoring Research Articles

Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis

The majority of senior persons are suffering a loss of physical condition their health is vital, the prevalence of illnesses has increased in the current age compared to previous generations, making health management in hospital sector. One of the concepts that can harness the advantages of the Internet of Things to better the older lifestyle is health monitoring for active and aided functioning. This approach aims to create a wireless communication system designed to remotely monitor patients. Each patient is wirelessly monitored based on Body temperature is measured via a temperature sensor, which gives an accuracy of around some certain value. The sensor carries a heart rate monitor with a pulse oximetry sensor. There are two light-emitting in it; one produces red light and the other infrared light. For tracking the amount of oxygen in the blood, two infrared LEDs are needed, but only one is needed to calculate the heart rate. The ESP 8266 (Node MCU) controller gathers the data and sends it to the specific application and Communication will be sent again to the consumers based on the data examination. The outcome results manage each patient's data separately and are used in hospitals, old age homes, and physically challenged people. Every sensor of data, such as temperature, heart rate, and abnormal heartbeat, will be managed in the database processed with GPS coordinates and data analyses using Think Speak Web Application is for informing about personal health data.

Validity of the Fitbit Inspire 2 heart rate monitor: impact of device placement, wrist movement, and sport activities

Validity of the Fitbit Inspire 2 heart rate monitor: impact of device placement, wrist movement, and sport activities

Transcranial magnetic stimulation in the assessment of acupuncture effect on exercise-induced fatigue.

Acupuncture as a traditional Chinese medicine therapy relies on unique theories to alleviate fatigue. The aim of this study is to evaluate the effect of acupuncture on exercise-induced fatigue utilizing transcranial magnetic stimulation (TMS). A total of 20 participants with regular exercise habits were recruited for this study. All participants were randomly assigned to receive either acupuncture or sham acupuncture intervention for exercise-induced fatigue. TMS and a heart rate monitor were used to measure the amplitude and latency of motor evoked potential (MEP) as well as heart rate every 5min over a 30-min period. The blood lactic acid (BLA) levels were measured using Lactate Scout+ at baseline, 0min, and 30min after fatigue. Two-way repeated measures analysis of variance was utilized to compare the differences between the effects of acupuncture method and time. Bonferroni post hoc tests were conducted to compare specific differences. Statistical significance was set at p<.05. Interaction effect was observed between acupuncture method and time effect in terms of amplitude (F(1, 38)=5.40, p<.001, η2=0.12) and latency (F(1, 38)=3.78, p=.008, η2=.09) of MEP. The application of acupuncture can promote the recovery of heart rate especially at 30min (p<.05), but which seem insufficient to generate significant difference in BLA (F(1, 38)=0.067, p=.797, η2=0.002). Acupuncture can promote the increase of MEP amplitude, shorten MEP latency, and restore heart rate. Preliminary findings provide novel insights for individuals with exercise habits to alleviate fatigue and enhance sports performance.

CSP2023: 426 - An international survey of experiences and attitudes towards Pacing using a heart rate monitor for people with ME / CFS

CSP2023: 426 - An international survey of experiences and attitudes towards Pacing using a heart rate monitor for people with ME / CFS

The influence of virtual reality simulation on surgical residents’ heart rate during an assessment of arthroscopic technical skills: A prospective, paired observational study

HypothesisTo demonstrate that a virtual reality (VR) simulation training program reduces heart rate variability during an assessment of surgical trainees’ technical skills in arthroscopy. Study designProspective observational matched study. Materials & methodsThirty-six orthopaedic surgery residents, new to arthroscopy, received standard training in arthroscopic knee surgery, supplemented by additional monthly training for 6months on a VR simulator for 16 of them. At inclusion, the 2 groups (VR and NON-VR) answered a questionnaire and performed a meniscectomy on a VR simulator. After 6months of training, two independent trainers blinded to the inclusion arms evaluated the technical skills of the two groups during meniscectomies on a model and on an anatomical subject. Heart rate variability (HRV) was measured using a wireless heart rate monitor during baseline, VR training, and assessment. ResultsAfter removing incomplete data, the analysis focused on 10 VR residents matched at inclusion with 10 NON-VR residents. The VR group had a significantly lower heart rate at the final assessment (p=0.02) and lower overall HRV (p=0.05). The low/high frequency ratio (LF/HF) was not significantly different between the groups (1.84 vs 2.05, p=0.66) but the before-after training comparison showed a greater decrease in this ratio in the VR group compared to the NON-VR group −0.76 (−41%) vs −0.08 (−4%). ConclusionThis study demonstrates a significant difference in heart rate variability between trained residents versus untrained residents during the final assessment of their technical skills at 6months. It appears that improving stress management should be an integral part of training programs in arthroscopic surgery. Clinical InterestVR simulators in arthroscopy could improve non-technical skills such as heart rate variability, from the perspective of accountability. Level of evidenceIII.

A 2.3-5.7 μW Tri-Modal Self-Adaptive Photoplethysmography Sensor Interface IC for Heart Rate, SpO2, and Pulse Transit Time Co-Monitoring.

This paper presents a tri-modal self-adaptive photoplethysmography (PPG) sensor interface IC for concurrently monitoring heart rate, SpO2, and pulse transit time, which is a critical intermediate parameter to derive blood pressure. By implementing a highly-reconfigurable analog front-end (AFE) architecture, flexible signal chain timing control, and flexible dual-LED drivers, this sensor interface provides wide operating space to support various PPG-sensing use cases. A heart-beat-locked-loop (HBLL) scheme is further extended to achieve time-multiplexed dual-input pulse transit time extraction based on two PPG sensors placed at fingertip and chest. A self-adaptive calibration scheme is proposed to automatically match the chip's operating point with the current use case, guaranteeing a sufficient signal-to-noise ratio for the user while consuming minimum system power. This paper proposes a DC offset cancellation (DCOC) approach comprised by a logarithmic transimpedance amplifier and an 8-bit SAR ADC, achieving a measured 38 nA residue error and 8.84 μA maximum input current. Fabricated in a 65nm CMOS process, the proposed tri-modal PPG sensor interface consumes 2.3-5.7 μW AFE power and 1.52 mm2 die area with 102dB (SpO2 mode), 110-116 dB (HR & PTT mode) dynamic range. A SpO2 test case and a HR & PTT test case are both demonstrated in the paper, achieving 18.9 μW and 43.7 μW system power, respectively.

Child Safety Monitoring and Notification Gadget in Smart Town/City using IoT

This study suggests a brand-new child safety device for smart cities that uses the Internet-of-Things (IoT) to give parents real-time monitoring and alerts about the child. With the use of a variety of sensors, including a temperature sensor to monitor ambient conditions, a touch sensor to detect simple presses or contact on the device's surface, a heartbeat sensor to measure fundamental health, and GPS for location tracking, this device is able to collect vital data about the child's well-being. Parents can view their child's data remotely. The microcontroller unit processes the data and sends it to a specified server. To notify parents of possible risks, such as exceeding a set temperature threshold, detecting a fall, or the child wandering beyond approved safe zones, a notification system is put in place. The device can also have a buzzer installed for instant local alerts in an emergency. Using the user-friendly Firebase software, parents can track their child's whereabouts continuously with the device's GPS module. This all-inclusive strategy improves child safety within the smart city infrastructure by giving parents peace of mind and enabling prompt action when needed. This device aligns with the expanding trend of smart city development, which uses technology to enhance the safety and well-being of its residents. Through its smooth integration with current IoT networks and infrastructure, this solution provides a useful and efficient way to protect kids in cities.

Ferric carboxymaltose: A game changer in the management of iron deficiency anaemia in pregnancy.

A single-arm prospective cohort (before-after) study among 60 antenatal women with moderate to severe anaemia in the second and third trimesters was conducted from December 2020 to December 2022, and the eligible women were given 1000 mg of intravenous FCM injection. Efficacy was assessed by rate of improvement in haemoglobin and ferritin at 2 weeks post infusion. Safety analysis was done by assessing adverse drug reactions and foetal heart monitoring during the infusion. A total 60 antenatal women with a median gestational age of 32.5 weeks at presentation received 1000 mg of intravenous FCM. There was a rise in mean haemoglobin from 8.05 gm% pre-infusion to 10.93 gm% 2 weeks post infusion, showing a mean rise of 2.88 gm%. Similar improvement was noted in mean serum ferritin levels from 25.92 pre-infusion to 253.96 post FCM infusion. There were no reports of drug-related major adverse effects in the mother or the foetus. FCM is found to be safe and effective treatment with rapid replenishment of haemoglobin and ferritin levels in a single dose, which makes it suitable and compels consideration as the first choice for treatment of iron-deficiency anaemia.

CADCare: Smart System for CHD Identification &amp; Sensor Alerts

INTRODUCTION: Cardiovascular diseases, particularly coronary artery disease (CAD), present a global health challenge, necessitating effective detection and diagnosis methods for early intervention. Various machine learning and deep learning approaches have emerged, utilizing diverse data sources such as electrocardiogram (ECG) signals and clinical features to enhance CAD detection. Additionally, circadian heart rate variability (HRV) has been explored as a potential diagnostic marker for CAD severity. This research aims to contribute to the burgeoning field of medical AI and its application in cardiology.OBJECTIVES: This study seeks to develop a Comprehensive Coronary Artery Disease Detection System integrating real-time heart rate monitoring and CAD prediction via an Android application. The objectives include seamless data transmission, efficient cloud-based data management, and the utilization of AI models, including ANNs, CNNs for ECG images, and hybrid models combining clinical and ECG data, to improve early CAD detection and management.METHODS: The system architecture involves integrating key sensors, an Arduino microcontroller, a Bluetooth module, and AI models to facilitate early CAD detection. An Android application complements the system, offering heart rate monitoring and CAD prediction using various data sources. Cloud computing is employed for efficient data management and analysis.RESULTS: The developed system successfully integrates cutting-edge technology to enhance CAD detection, achieving accurate and efficient results in real-time heart rate monitoring and CAD prediction.CONCLUSION: The Comprehensive Coronary Artery Disease Detection System, leveraging AI and cloud computing, contributes to proactive health monitoring and informed decision-making in CAD management and prevention, thereby addressing a critical need in cardiovascular health care.

Kinematic and Physiological Analysis of Medieval Combat Sport Using Motion Analysis, Blood Lactate Measurement, and Heart Rate Monitoring: A Case Study.

Medieval combat sport is a form of mixed martial art in which combatants engage in fighting using offensive and defensive equipment while dressed in full armor. The sport is considered extremely taxing, making it nearly impossible to maintain the same level of performance. However, this form of sport has not been thoroughly analyzed, and its impact on human physical response is largely unknown. To address this gap, the study reported here aimed to introduce and test a procedure for analyzing human physical responses within the framework of the sport. To accomplish this, two experienced combatants were asked to engage in a series of strikes, performed in the form of a set duel simulating a professional fight competition. The kinematic aspect of the procedure was examined using motion analysis with the help of an IMU suit, while the physiological aspect was evaluated based on blood lactate levels and heart rate measurements. Furthermore, an ergometer test conducted in a laboratory setting aimed to determine the lactate threshold. The duel results showed noticeable decreases in the kinematic aspects of the strikes, such as the velocity of impact, and a dramatic rise in physiological aspects, such as heart rate and blood lactate levels. During the duel sets, the blood lactate surpassed the threshold level, and at the end, the heart rate exceeded the maximum age-related level. Practicing medieval combat sport has been shown to impose an extreme physical load on the bodies of combatants, noticeably affecting their performance levels.

Research and Design of A Multifunctional Heart Rate Detection System

Addressing the limitations of conventional heart rate detectors, which solely monitor heart rate without providing alerts during critical incidents, a multifunctional heart rate detection system has been developed. This system incorporates a GD32 as the main controller and utilizes a pulse sensor for heart rate monitoring, an MLX90614 infrared temperature sensor for body temperature measurements, an MPU6050 acceleration sensor for motion posture detection, and a SIM868 module for GPS positioning and server communication. This comprehensive setup allows for real-time monitoring of the wearer's heart rate and body temperature, assessment of fall incidents based on motion posture data, and regular uploading of health data to a server. Additionally, the system is equipped with a one-touch emergency call feature. In the event of abnormal heart rate readings or a fall, it triggers an alarm, acquires GPS coordinates, and automatically dials an emergency contact number. After thorough testing and verification, the system has demonstrated full functionality with heart rate data accuracy within the expected error range, offering a reliable safety mechanism for patients while on the move.

A deep learning-assisted skin-integrated pulse sensing system for reliable pulse monitoring and cardiac function assessment

Long-term pulse monitoring plays a vital role in the prevention and diagnosis of cardiovascular diseases since pulse signals can provide rich characteristics of cardiac conditions. Flexible sensors mounting on the wrist for pulse monitoring have drawn great attention due to the advantages of soft, skin-interfaced and non-invasive features. However, slight wrist movement and transposition of the sensor on skin would dramatically impact the signal quality because of motion artifacts and interfacial properties. Here, we report an intelligent skin-integrated system by combining a sensitive pressure sensor array and a signal enhancement algorithm to provide reliable pulse signal monitoring. The sensors have the advantages of low cost, simple fabrication, reusability and high sensitivity. The multiple sensor channels offer rich information of vital signals, which can facilitate deep learning-based algorithms to denoise and reconstruct pulses more accurately and thus enable precise heart rate monitoring and heart variability assessments. The reported technology suggests a novel approach for pulse waveform detection in static and dynamic conditions, allowing users to more easily achieve reliable pulse monitoring.

AI-based heart monitoring: the FIFPRO player pilot study

Abstract Background The implementation of remote global heart screening and monitoring services in professional players is essential as it allows: (1) efficient and rapid identification of cardiac abnormalities ensuring their safety and health; (2) standardizing the quality of medical heart checkups in low-resource regions to promote health equity; (3) prioritizing diversity in data collection to aid in the research and prevention of sudden cardiac death in sports. Purpose To demonstrate the feasibility of a global standardized heart screening program for professional footballers by leveraging an artificial intelligence platform to efficiently identify cardiac patterns from Holter’s records. In this study, we presented the most prevalent cardiac patterns, the patterns that prompted further assessment by the cardiologist, and an example of a typical research question exploring the effect of age on the prevalence of cardiac patterns. Methods The pilot study included 100 players from 9 different countries: Australia, Botswana, Bulgaria, Ghana, Greece, Indonesia, Panama, Spain and the UK. Holter data (Bittium Faros 180) were acquired in real-life monitoring from 24h to 48h including training sessions. Electrocardiograms were analyzed through an AI platform with a deep learning-based methodology to detect over 20 cardiac patterns. Medical team reviewed labels of detected cardiac patterns to validate and further train the AI algorithm. Thereafter cardiologists reviewed the cardiac patterns to determine whether further cardiac assessment was required. Results 145 sessions were recorded, with 100 players having one session and 45 players having two sessions. The mean age was 27.5 years (minimum 15, maximum 41), with 31 female players. The median duration of the Holter session was 25.1 hours. Table 1 presents the most frequently detected cardiac patterns: low density premature atrial complex, mild sinus bradycardia and low density premature ventricular complex in over 50% of the players. 18 players were identified with abnormalities requiring further study. 14 presented altered cardiac patterns (6 with negative T-waves, 5 with intraventricular conduction delay, 2 with QRS low voltage and 1 with P wave enlargement) and 3 presented premature ventricular complexes during exercise or high density. 1 was excluded because of inadequate clinical information. Regarding the effect of age, players with mild sinus bradycardia, premature ventricular complex, pause (2-3 seconds) or 2-degree atrioventricular block were found to be significantly older than players without those patterns (Figure 1). Conclusion This is the first global AI-based platform to obtain standardized heart screening program in professional athletes remotely. This study has been validated using diverse data from groups on 5 continents. This approach is beneficial to promote health equity in screening and monitoring of possible cardiomyopathies and channelopathies in order to prevent sudden cardiac death.Table 1Figure 1

Heart rate is an important predictor of pacing induced cardiomyopathy in a machine learning analysis

Abstract Background 10-20% of patients who have right ventricular pacing develop pacing induced cardiomyopathy (PICM). Home monitoring (HM) could identify patients developing PICM in real-time and facilitate echocardiographic confirmation, medication optimisation and if necessary cardiac resynchronisation therapy (CRT). Purpose To identify predictors of PICM using machine learning models. Methods Consecutive cases of patients who developed PICM (12) after dual chamber pacing were identified from our implant database and matched 4:1 with propensity matched controls who underwent pacing but did not develop PICM (48) (Table 1.) HM data from the 6 months prior to echocardiographic diagnosis of PICM was analysed. PICM was defined as (1) a deterioration in left ventricular (LV) ejection fraction (EF) of &amp;gt;10%, (2) to an EF of &amp;lt;45%, (3) with right ventricular (RV) pacing &amp;gt;20% and (4) the exclusion of alternative causes. Three types of ML models were generated (1) random forest, (2) extreme gradient boosted (XGBoost) (3) K-nearest neighbours algorithm (KNN), with 10-fold cross-validation. Results HM data were available for 12 PICM cases (75 ± 8 years, 72% male). PICM occurred at 2.0 ± 1.2 years. There was no difference between cases and controls in atrial pacing (8±15% vs 18±22% p=0.14), ventricular pacing (81±29% vs 85±24% p=0.62) or base rate (53±5bpm vs 53±5bpm p=0.80). Of the 34 RM features analysed, mean ventricular heart rate was identified as the most important predictor of PICM; area under curve with XG Boost 0.95, KNN 0.95, and random forest 0.86. Other RM features failed to add any more predictive power to the models. Mean ventricular rate was significantly higher in cases than controls (80±6 bpm vs 69±9 bpm p&amp;lt;0.001). Sensitivity analysis was performed to assess whether episodes of atrial fibrillation (AF) were responsible for the higher ventricular rates. After days of AF days were excluded, mean ventricular rate was still significantly higher in cases than controls (79± 6vs 69±9bpm p&amp;lt;0.001) (Figure 1). 10/12 patients had a subsequent CRT upgrade. No consistent pattern emerged in heart rate changes in the first 3 months after CRT upgrade. Conclusion In a group of patients with normal left ventricular function at implant, remotely monitored heart rate can predict early-onset PICM. Further work is required to understand when these heart rate differences emerge, and whether they improve with positive LV remodelling.Table 1.Figure 1.

#1391 Evaluating effect on frailty of a home-based training program developed for kidney transplant recipients (HOMETRAIN-KTR)

Abstract Background and Aims Frailty and impaired physical function are prevalent among patients undergoing kidney transplantation (KTx), and is an independent risk factor for poor outcome after transplantation. Limited number of studies have prospectively investigated the efficacy of exercise training for KTx-recipients with low pre-transplant physical function. Our aim is to evaluate the effect of a structured 12 week home-based physical training program delivered to recipients with low pre transplant physical function, on physical function and frailty at 1-year post transplant. Method HOMETRAIN-KTR is a 2-arm unblinded Norwegian clinical trial with RCT design (Fig. 1). Patients will be recruited at admission for KTx at Oslo University Hospital, Rikshospitalet. Patients who score below 60 at 36-Item Short Form Survey (SF-36) will be included and tested with 6-minute walking test (6MWT), Clinical Frailty Scale (CFS), hand-grip strength, 30 seconds Sit-to-Stand test, and questionnaire about physical activity and psychological distress. Included patients are randomized to either training group or control group (standard of care). The training group will, under supervision by a physiotherapist, start a 12-week structured home-based training program from week 4 post-KTx. The training program consist of a combination of strength training, cardio training and optional activities. During the intervention period, the intervention group will are receive regular follow-up by phone. The training sessions will be documented via patients logs and heart rate monitor (Fitbit). At one year post-KTx, all patients will undergo the same tests as at inclusion. Results Inclusion of patients will start in January 2024, and continue for the next 2-3 years. We aim to include 200 patients, 100 in each arm. The study will evaluate physical function, frailty, graft function, patient survival, HRQoL and physiological distress during the time-span of the study. Conclusion The HOMETRAIN-KTR study is supposed to bring new knowledge about the effect of a physical training program on frail KTx-recipients. Such information is highly relevant to improve KTx outcomes and to optimize the utilization of the limited source of donor organs.

Echocardiographic monitoring of myocardial function in a female patient with right heart Loeffler endocarditis at thrombotic stage after Epstein-Barr-virus infection

BackgroundTransthoracic echocardiography is usually the first non-invasive imaging modality for the detection of Loeffler endocarditis at thrombotic stage. In the recent decade 3D echocardiography and deformation imaging already proved as a helpful tool for the monitoring of left and right ventricular heart disease.Case presentationThe present case illustrates the diagnostic role of 3D echocardiography and deformation imaging in the acute stage of right sided Loeffler endocarditis in a 70-year-old Western European (German) woman. This case proves that myocardial involvement due to inflammation can be detected at subclinical stages by speckle tracking echocardiography. Acute deterioration of left and right ventricular function and the early response to prednisolone therapy can objectively be monitored. In addition, alterations of effective stroke volume can quantitatively be assessed by 3D right ventricular volumetry with exclusion of thrombus formation in the volume measurements.ConclusionThis case underlines the importance of 3D echocardiography and deformation imaging as a helpful diagnostic tool in disease management in the acute phase of Loeffler endocarditis at thrombotic stage.

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.

Review on Pregnancy Progress Monitoring

This project outlines the creation of an innovative pregnancy progress monitoring system leveraging cutting-edge sensor technology and artificial intelligence (AI) models. This project presents an integrated system designed to revolutionize the monitoring of pregnancy progression through a sophisticated fusion of sensor technology and artificial intelligence (AI). The collaborative functionality of DS18B20 temperature sensors, heart beat sensor, and GSM modules establishes a seamless data collection and transmission framework. These sensors continuously capture vital parameters including body temperature, heart rate, and blood oxygen saturation, while the GSM module ensures secure and real-time transmission of this data to designated servers or caregivers’ devices. Leveraging AI-driven algorithms, the system interprets these multifaceted datasets, employing pattern recognition and anomaly detection to discern potential health irregularities. The intuitive user interface grants caregivers comprehensive access to visualized data, facilitating proactive monitoring and swift response through customizable alerts for critical health indicators. This innovative solution undergoes iterative development and validation, seeking to redefine prenatal care by enabling early identification of deviations and fostering informed decision-making among medical practitioners and expectant parents. By amalgamating sensor technology with AI capabilities, this system aspires to enhance the safety and well-being of both mother and fetus, marking a paradigm shift in prenatal health monitoring and intervention strategies.

A clinical evaluation and acceptability study of the innovative SurePulse VS wireless heart rate monitor across the neonatal journey.

The SurePulse vital signs (VS) device is an innovative wireless heart rate monitor designed for neonatal patients. This study evaluates the application of SurePulse VS technology in clinical practice. Data were collected about the quantitative metrics of the device itself when deployed on real infants and qualitative feedback from perinatal professionals and parents regarding their experiences using this novel technology. This study recruited 101 infants and achieved target completion rates of 101 healthcare professional (HCP) and 51 parent questionnaires over the seven-month study period. The SurePulse device was deployed across a range of gestational ages (34-39 weeks) and birth weights (1.8-3.5 kg). Device deployment was performed across a range of clinical environments, with 51% of deployments at delivery and 47% within the neonatal unit. The data show clinically acceptable timings from device deployment to heart rate signal acquisition [median 20 s (IQR 15-76 s)]. HCP feedback rated SurePulse monitoring as "Always" or "Mostly" reliable in 80% of cases. Parental feedback reported that having the SurePulse device was reassuring, convenient and beneficial to them. These positive comments were reflected across device deployment in the delivery room and within the neonatal unit. The study findings show that the SurePulse device has potential to be a significant advancement in the way neonatal patients are monitored in a variety of post-delivery circumstances. This study has demonstrated that the SurePulse device has utility throughout the neonatal journey, enabling accurate heart rate monitoring in a manner that promotes parent-infant contact and bonding.

Measuring Physical Activity in Children Who Do Not Walk and With Cerebral Palsy: An Exploratory Case Series.

The purpose of this case series was to describe physical activity (PA) amount and intensity in the home and school environment. Accelerometers and heart rate (HR) monitors are reliable and valid measures of PA in children with cerebral palsy (CP) who can walk. There is limited research on PA measures in children with CP who cannot walk. Three 9-year-old boys with CP, Gross Motor Function Classification System levels IV and V, participated in a 1-week measurement period wearing waist- and wrist-worn triaxial accelerometers to measure PA counts and a wrist-worn HR monitor to measure PA intensity. PA intensity was calculated using an estimated HR max. Accelerometer counts were reported. Parents and school staff completed activity and eating journals. Six days of PA and HR data were analyzed. Two participants spent more time in moderate/vigorous PA intensity during school compared to at home. Activities with greatest PA intensity included oral eating, communication, and social engagement. Higher activity counts were recorded from the wrist compared with the waist accelerometers. PA and eating journal adherence were high in both settings. The findings provide preliminary data to evaluate PA amount and intensity in children with CP who have little walking ability. It is important to consider PA levels in daily activity for youth with CP when designing plans of care.