A digital nervous system aiming toward personalized IoT healthcare.

Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing-from multiple sensors-are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system.

Human body and head characteristics as a communication medium for Body Area Network.

An in-depth investigation of the Body Channel Communication (BCC) under the environment set according to the IEEE 802.15.6 Body Area Network (BAN) standard is conducted to observe and characterize the human body as a communication medium. A thorough measurement of the human head as part of the human channel is also carried out. Human forehead, head to limb, and ear to ear channel is characterized. The channel gain of the human head follows the same bandpass profile of the human torso and limbs with the maximum channel gain occurring at 35MHz. The human body channel gain distribution histogram at given frequencies, while all the other parameters are held constant, exhibits a maximum variation of 2.2dB in the channel gain at the center frequency of the bandpass channel gain profile.