# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 6 Design of the Flexible Wearable Heart Rate Signals Transmission and Output Module
### Low-Power Hardware Design for Flexible Wearable Heart Rate Signal Transmission and Output Module
Its self-organizing network architecture supports multi-hop relay transmission, enabling the relay of heart rate signals among dozens of collection nodes without a fixed infrastructure. Meanwhile, the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism avoids power consumption waste caused by collision retransmissions. However, in scenarios with multiple users or crowded radio frequency (RF) environments, such as hospitals and gyms, wireless channels are vulnerable to co-channel interference, multipath fading, and system collisions, which may lead to interruptions or distortion in heart rate data transmission and conflict with the core goal of continuous monitoring. The specific manifestations include the following: packet loss: in dense deployments of BLE/Wi-Fi/ Zigbee modules, channel competition can increase the loss rate of critical heart rate data [129]; increased transmission delay: interference-induced retransmission mechanisms prolong data transmission time, and if the delay exceeds the tolerance threshold of the monitoring system (real-time heart rate alerts require <500 ms) [130], it may affect the timeliness of clinical decision-making; and additional power consumption loss: frequent retransmissions or continuous channel scanning can increase the dynamic power consumption of wireless modules, and studies have shown that each additional retransmission can cause the power consumption of BLE modules to rise by 15-20% [131], conflicting with the goal of low-power design. In response to the issues such as data packet loss, increased transmission delay, and additional power consumption loss caused by BLE, Wi-Fi, and Zigbee modules when radio frequency signals are congested, as mentioned in the aforementioned literature [122; 124; 125; 126; 127; 128], this paper has collated and summarized a set of channel robustness optimization strategies for each module. For BLE modules, which are prone to data loss due to 2.4 GHz frequency band conflicts in crowded RF scenarios, existing studies balance robustness and low-power consumption through two strategies. Adaptive frequency hopping (AFH) technology, such as the AFH mechanism supported by the CC2541 chip, can dynamically avoid interference bands such as channels interfered by MRI equipment in hospitals; by pre-setting frequency hopping maps to reduce channel scanning time, it lowers collision probability by 30% compared to fixed frequency hopping schemes, with additional power consumption controlled within 0.2 mW as hopping decisions rely on local channel quality detection without cloud interaction [132]. Dynamic transmission power adjustment, which adaptively reducestransmission power based on Receiver Signal Strength Indication RSSI at the receiving end, reduces interference to other systems while ensuring communication distance, lowering the average power consumption of BLE modules in crowded environments from 3 mW to 2.5 mW [133]. For Wi-Fi modules, to tackle interference issues under high loads, a low-power design needs to combine channel selection and protocol optimization. It can employ a real-time channel quality evaluation mechanism in its heart rate cloud monitoring module, identifying idle channels through intermittent monitoring; the switching process consumes only 0.5 mW of instantaneous power, reducing energy consumption by 60% compared to continuous channel scanning [134]. For Zigbee modules, which improve robustness through self-organizing network characteristics in complex monitoring networks while maintaining low-power consumption, the enhanced CSMA/CA mechanism introduces a \"dynamic backoff window adjustment\" strategy on the basis of traditional collision avoidance. When channel busy is detected, the backoff window expands exponentially to reduce invalid competition; during idle periods, the window shrinks quickly to lower waiting energy consumption, reducing the collision rate from 12% to 5% in 20-node networks with an average power consumption increase of no more than 0.3 mW [135].