# Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport
###### Abstract  
The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capabilities. This paper proposes integrated energy-aware techniques and a convolutional neural network (CNN) for a cardiac arrhythmia detection system that can be worn during sport training sessions. The dynamic power management strategy (DPMS) is programmed into an ultra-low-power microcontroller, and in combination with a photovoltaic (PV) energy harvesting (EH) circuit, achieves a battery-life extension towards a self-powered operation. The CNN-based analysis filters, scales the image, and using a bicubic technique, interpolates the measurements to subsequently classify the electrocardiogram (ECG) signal into normal and abnormal patterns. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy available, which represents 12 consecutive workouts of 45 min without the need to manually recharge it. Furthermore, an arrhythmia detection precision of 98.6% is achieved among the experimental sessions using 55,222 images for training the system with the MIT-BIH, QT, and long-term ST databases, and 1320 implemented on a wearable system. Therefore, the proposed wearable system can be used to monitor an athlete's condition, reducing the risk of abnormal heart conditions during sports activities. capcaption=true,to distinguish between normal and pathological heart activity, then the influence of type and length of exercise in athletes can be assessed with the classification of ECG signals during training sessions [4; 11]. In this regard, Artificial Intelligence (AI) has created new opportunities to automate and expedite the solution of complex problems in medical practice that in the past, depended heavily in human expertise. For example, pattern recognition with deep neural networks can be used to help interpret images from medical scans, X-rays, tissue samples, among others, improving diagnosis accuracy and reducing the possibility of human error [12; 13; 14; 15]. Neural networks can also be used to interpret different vital bio signals. Different studies to classify ECG signals have already been proposed, using optimum-path forest (OPF) [16], Hidden Markov models (HMM) [17], independent component analysis (ICA) [18], cluster analysis (CA) [19], probabilistic neural networks (PNN) [20], recurrent neural networks (RNN) [21] and support vector machines (SVM) [22]. However, none of these works develop an Internet of Things (IoT) wearable solution leveraging such AI methods. The artificial intelligence-of-things (AIoT) combines AI with IoT technology. AloT uses the existing IoT standards with a wearable embedded system to support intelligent data exchange between devices [23; 24]. In recent years, diverse IoT wearable systems for ECG signal acquisition and analysis were proposed [25; 26; 27; 28; 29; 30]. Their integration usually contains a low-power microcontroller (MCU), sensors, and smartphone interfaces with low-energy wireless data communication to store data in real time and exchange information with other devices. However, energy-aware techniques for a battery extension operation are not always incorporated, and the use of machine learning on sports wearable devices has not been fully explored [31]. The complexity lies in the amount of data required in artificial intelligence algorithms and the low-power processing for wearable electronic systems. Therefore, the challenge remains in developing energy-saving techniques for an autonomous, smart detection of underlying cardiac pathologies that may lead to increased risk in the athlete. Such a system must consist of wearable devices with the potential to perform AI pattern recognition. These features would allow the real-time monitoring of ECG signals while exercising for long periods, and detecting the pathological physiology of heart activity.