Alarm warning dan rem otomatis berbasis detak jantung pada olahraga bersepeda

Jantung merupakan organ vital pada tubuh manusia. Oleh karena itu, penting dalam memelihara kesehatan jantung. Salah satunya melalui pelatihan berbasis detak jantung, yaitu menjaga detak jantung dalam kisaran yang ditetapkan. Salah satu pengaplikasiannya adalah olahraga bersepeda, namun tidak jarang pesepeda melebihi target maksimal saat bersepeda, sehingga hal tersebut menyalahi aturan dalam pelatihan. Prinsip kerja alat ini adalah mengaktifkan alarm warning jika detak jantung pengguna melebihi target maksimal saat latihan dan mengaktifkan rem otomatis ketika pengguna menghiraukan alarm warning. Target latihan diperoleh dari perhitungan umur, maximal heart rate (MHR), Rest Heart Rate (RHR), dan riwayat latihan. Penelitian ini menggunakan grove heart rate sensor sebagai sensor detak jantung dengan mikrokontroler Arduino MEGA. Proses pengambilan maupun penyimpanan data pada database dilakukan oleh NodeMCU. Motor DC 12V digunakan sebagai penarik rem dengan driver motor sebagai pengatur arah dan putaran motor. Berdasarkan hasil pengujian diperoleh bahwa data detak jantung peserta latihan tersimpan dalam database sistem, alarm warning bekerja(buzzer on) jika detak jantung peserta diatas 135 bpm, dan rem otomatis bekerja pada saat detak jantung peserta latihan diatas 135 bpm dengan berubahnya panjang tali rem dari 15 cm menjadi 12 cm. The heart is a vital organ in the human body. Therefore, it is important in maintaining heart health. One of them is through heart rate-based training, which is keeping the heart rate within a specified range. One of its applications is cycling, but it is not uncommon for cyclists to exceed the maximum target when cycling, so that this violates the rules in training. The working principle of this tool is to activate an alarm warning if the user's heart rate exceeds the maximum target during training and activate the automatic brake when the user ignores the alarm warning. Training targets are obtained from the calculation of age, maximal heart rate (MHR), Rest Heart Rate (RHR), and training history. This study uses a grove heart rate sensor as a heart rate sensor with an Arduino MEGA microcontroller. The process of retrieving and storing data in the database is carried out by NodeMCU. DC 12V motor is used as a brake puller with the motor driver to control the direction and rotation of the motor. Base on the test result, it is found that the participant’s heart rate data is stored in the database system, the alarm warning work (buzzer on)if the participant’s herat rate is above 135 bpm, and the brakes automatically work when the participant’s herat rate is above 135 bpm by changing the length of the brake rope from 15cm to 12 cm.

Technical Analysis and Heart Rate Response of Minifootball Players During a Competitive Match

Objectives: The aim of this study was to investigate the technical actions and heart rate response of amateur minifootball players during a competitive match. Methods: Ten male amateur minifootball players (age: 26.3 ± 4.2 years, height: 176 ± 7.4 cm and body mass: 78.9 ± 10 kg), from a First Division Tunisian team, participated in a match (2 × 25-min with 10 min rest). Heart rate was recorded during the whole time that participants spent on court. The minifootball match was recorded and analyzed for technical actions (i.e., data pertaining to passes, shots, actions, duels, fouls) using InStat. Results: Results showed that a minifootball match induced an intensity of 84.2% heart rate max (HRmax) (i.e., high intensity). The percentage of success in actions was 53.33 % and in shots was 36.54 %. A large number of passes (400 (290 successful)) and duels (210 (145 successful)) was found both in offensive and defensive phases. Conclusions: A minifootball match is of high intensity and requires a high level of physical and technical ability.

An AI-Based Exercise Prescription Recommendation System

The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs.

Investigating the effect of the body mass index (BMI) values on the behavior of human energy expenditure

Employee durability is a critical factor to improve a company performance. Company management must control employee health conditions. The purpose of this paper is to determine the effect of office worker’s BMI variation on human energy expenditure behavior including the recovery process. This study started with literature reviews of BMI, human biology, energy expenditure, and physiology ergonomics. The data was collected randomly from 126 nonphysical office workers in productive ages from 20 to 40 years old. The BMI, resting heart rate, activity heart rate, and recovery heart rate of all respondents then recorded. The results shows that the respondents BMI scores are classified into underweight (BMI <18.5) with totaling = 4%, healthy weight (18.5 ≤ BMI ≤ 22.9) = 34.1%, light obesity (23 ≤ BMI ≤ 24.9) = 23%, medium obesity (25 ≤ BMI ≤ 29.9) = 29.4%, and weight obesity (BMI> 30) = 9.5%. The underweight class has the lowest average rest heart rate = 68.6 bpm and the overweight class has the highest average rest heart rate = 84.6 bpm. Consequently, heart rate during activity for each class from underweight to overweight is 88.4 bpm, 90.9 bpm, 93.3 bpm, 95.1 bpm, and 98.6 bpm. With the same order, the heart rate reduction percentage during the recovery phase is 4.6%, 11.0%, 13.1%, 16.0%, and 8.8%. In brief, the BMI variation strongly correlated with Time to Recovery (TTR) of nonphysical office workers.

Resting Heart Rate and Auditory Evoked Potential

The objective of this study was to evaluate the association between rest heart rate (HR) and the components of the auditory evoked-related potentials (ERPs) at rest in women. We investigated 21 healthy female university students between 18 and 24 years old. We performed complete audiological evaluation and measurement of heart rate for 10 minutes at rest (heart rate monitor Polar RS800CX) and performed ERPs analysis (discrepancy in frequency and duration). There was a moderate negative correlation of the N1 and P3a with rest HR and a strong positive correlation of the P2 and N2 components with rest HR. Larger components of the ERP are associated with higher rest HR.

Assessing Physical Activity during Wheelchair Pushing: Validity of a Portable Accelerometer

We assessed the validity of the Computer Science and Applications, Inc. (CSA) accelerometer as a measure of energy expenditure during wheelchair pushing. Participants completed three timed pushes over an indoor course at three different speeds while wearing a CSA accelerometer on both wrists. Pushing speeds were assigned in a random order and separated by a 5–10 min rest. Heart rate and energy expenditure were measured using an Aerosport TEEM 100. Results indicated pushing speed, heart rate, and oxygen consumption increased significantly over the three conditions (p < .01). Significant associations (p < .01) were noted between CSA readings from both wrists and energy expenditure over the three pushing speeds (left wrist, r = .66, right wrist r = .52). These results suggest that the CSA accelerometer worn at the wrist may provide a useful measure of physical activity in persons who use wheelchairs as their primary mode of locomotion.

Epinephrine, norepinephrine, and dopamine during a 4-day head-down bed rest

Head-down bed rest at an angle of 6 degrees was used as an experimental model to simulate the hemodynamic effects of microgravity, i.e., the shift of fluids from the lower to the upper part of the body. The sympathoadrenal activity during acute (from 0.5 to 10 h) and prolonged (4 days) head-down bed rest was assessed in eight healthy men (24 +/- 1 yr) by measuring epinephrine (E), norepinephrine (NE), dopamine (DA), and methoxylated metabolite levels in their plasma and urine. Catecholamine (CA) and methoxyamine levels were essentially unaltered at any time of bed rest. Maximal changes in plasma were on the second day (D2): NE, 547 +/- 84 vs. 384 +/- 55 pg/ml; DA, 192 +/- 32 vs. 141 +/- 16 pg/ml; NS. After 24 h of bed rest, heart rate decreased from 71 +/- 1 to 63 +/- 3/min (P less than 0.01). Daily dynamic leg exercise [50% maximum O2 uptake (VO2 max)] used as a countermeasure did not alter the pattern of plasma CA during bed rest but resulted in a higher urinary NE excretion during postexercise recovery (+45% on D2; P less than 0.05). The data indicate no evident relationship between sympathoadrenal function and stimulation of cardiopulmonary receptors or neuroendocrine changes induced by central hypervolemia during head-down bed rest.