Wearable computing has a great prospect in smart healthcare applications. The emergence of the Internet of Things, Wireless Body Area Networks (WBANs), and big data processing open numerous challenges and opportunities. In healthcare, the monitoring is done by placing/implanting sensor nodes (resource-constrained devices) on a patient’s body to communicate data to a resource-rich node called a sink. The data transmission energy consumption is directly proportional to the distance between the sensor and the sink node. Therefore, it is vital to reduce the energy consumption of the sensor node due to data transmission. In this article, a new Dual Forwarder Selection Technique (DFST) has been proposed to prolong the network lifetime by reducing energy consumption and ultimately improving the stability period and throughput of the network. The DFST works by grouping sensor nodes on a body where both forwarder nodes have been selected through a cost function for relaying data to the sink. The proposed scheme’s efficiency has been evaluated using simulation results in terms of network stability, lifetime, and throughput. Energy consumption of sensor nodes minimized, which, as a result, increased residual network energy. The number of dead nodes of the DFST is about 50% less than that of its counterparts RE-ATTEMPT and iM-SIMPLE. The average throughput of the proposed scheme is 51% and 8% higher than the methods. Similarly, the residual energy of the DFST is approximately 200% and 120% more than iM-SIMPLE and RE-ATTEMPT, respectively.