Worldwide, 16.7 million people die each year due to cardiovascular disease. These statistics raise demand of devices like sensor-based pacemakers (PM) which are not just doing heart rate augmentation but also capable to transmit information via wireless link to on body sensor and support remote monitoring of such patients. As per the world health organization WHO reports there are more than 3 million functioning PMs and about 600,000 pacemakers are implanted each year in world. On an average, 70–80% of PMs are implanted in aged patients around 65 years or older. In addition to continuous monitoring of cardiovascular parameters, detection of physical movement of such patients may be helpful to assess their well-being. This paper has been formulated with an aim to highlight an approach which may be used to detect the physical movement of the patient using information signal received from implanted PM. The transmitted signal will experience a pathloss offered by wireless human body channel, which will affect the link quality parameters namely Signal to Noise Ratio (SNR) and Bit Error Rate (BER) and received signal strength indicator (RSSI). In the current work mathematical model has been formulated considering in body and on body channel propagation conditions and received power, received energy, pathloss, SNR, BER, bit rate, energy per bit and RSSI have been evaluated using IEEE802.15.6 channel models CM2 and CM3. Data set has been created and human body movement has been detected using Machine Learning (ML) techniques. Prediction accuracy of Multilayer Perceptron (MLP), k-Nearest Neighbours (kNN) and Random Forest have been compared. The analysis performed depicts that human body movement can be detected using different channel models and ML techniques such as MLP, kNN and Random Forest with an accuracy of 65.3%, 72.8% and 93.4% respectively. The critical comparison of the result indicates that the performance of Random Forest is better than MLP and kNN. This approach will be helpful in remote detection of human body movement of patients.

全世界每年有1670万人死于心血管疾病。这些统计数据增加了对诸如基于传感器的起搏器（PM）等设备的需求，这些设备不仅可以进行心跳加速，而且还可以通过无线链接将信息传输到人体传感器上，并支持对此类患者进行远程监控。根据世界卫生组织的报告，世界上每年有超过300万个功能正常的PM和大约60万个起搏器被植入。平均而言，年龄在65岁或以上的老年患者中会植入70％至80％的PM。除了持续监测心血管参数外，检测此类患者的身体活动可能有助于评估其健康状况。制定本文的目的在于突出一种方法，该方法可用于使用从植入式PM接收到的信息信号检测患者的身体运动。传输的信号将遭受无线人体信道提供的路径损耗，这将影响链路质量参数，即信噪比（SNR）和误码率（BER）和接收信号强度指示器（RSSI）。在当前的工作中，已经考虑到身体和身体上的信道传播条件和接收功率，接收能量，路径损耗，SNR，BER，比特率，每比特能量和RSSI的公式，使用IEEE802.15.6信道模型CM2和CM2评估了数学模型。 CM3。已使用机器学习（ML）技术创建了数据集并检测了人体运动。比较了多层感知器（MLP），k最近邻（kNN）和随机森林的预测精度。进行的分析表明，可以使用不同的通道模型和ML技术（例如MLP，kNN和随机森林）检测到人体运动，其准确度分别为65.3％，72.8％和93.4％。结果的关键比较表明，Random Forest的性能优于MLP和kNN。该方法将有助于远程检测患者的人体运动。结果的关键比较表明，Random Forest的性能优于MLP和kNN。该方法将有助于远程检测患者的人体运动。结果的关键比较表明，Random Forest的性能优于MLP和kNN。该方法将有助于远程检测患者的人体运动。