Healing Hands: The Tactile Internet in Future Tele-Healthcare

Abstract

In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered significant momentum in research and implementations in the years preceding the worldwide challenge of 2020, supported by the emerging capabilities of communication networks. The Tactile Internet (TI) with human-in-the-loop is one of those developments, leading to the democratization of skills and expertise that will significantly impact the long-term developments of the provisioning of care. However, significant challenges remain that require today's communication networks to adapt to support the ultra-low latency required. The resulting latency challenge necessitates trans-disciplinary research efforts combining psychophysiological as well as technological solutions to achieve one millisecond and below round-trip times. The objective of this paper is to provide an overview of the benefits enabled by solving this network latency reduction challenge by employing state-of-the-art Time-Sensitive Networking (TSN) devices in a testbed, realizing the service differentiation required for the multi-modal human-machine interface. With completely new types of services and use cases resulting from the TI, we describe the potential impacts on remote surgery and remote rehabilitation as examples, with a focus on the future of tele-healthcare in rural settings.

Keywords: human-in-the-loop; multi-modal; multisensory perception; tactile internet; tele-healthcare.

Figure 1

Examples for Human-in-the-Loop (HiL) control loops illustrating human–machine cohabitation principles. Human operators performing surgeries by remotely controlling a surgical robot and human patients wearing sensor gloves in tele-rehabilitation providing data interpreted remotely resulting in real-time treatment adaptation. (a) Human operators remotely controlling machines and receiving feedback. (b) Machines controlling human actions and receiving feedback.

Figure 2

Breakdown of the total round-trip latency into the different latency components $t_p$, $t_m$, and $t_c$, when a transmitter communicates with a receiver.

Figure 3

Example measurement results for the tactile, audio, and video network traffic, individually prioritized. Without background traffic, the latency is predominantly driven by the Ethernet layer frames’ sizes and counts. With competing background traffic, the network switch handling of additional frames results in overall latency increases. While traffic prioritization does not separate well three slices of traffic, which is an unexpected result, traffic prioritization and time-aware shaper can separate three slices of traffic and benefit the sensitive tactile traffic the most. (a) Latency of three slices of traffic in four measurement scenarios. (b) Complementary Cumulative Distribution Function (CCDF) of three slices of traffic when the time-aware shaper is applied.

Figure 4

Example scenario for the increased reach of tele-healthcare in rural communities. While this example area in central Michigan is surrounded by hospitals, their individual reach (maroon) would only cover the closest areas, assuming a 15-km range to enable a 1-ms latency. Provided with nearby cellular towers, the majority of the region could be serviced as well within the latency requirements. Reducing the latency requirements incrementally would increase the coverage further.