IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring

Abstract

Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.

Keywords: IMU; free-living assessment; gait; internet of things; telemedicine.

Figure 1

In prescribing IMUs for gait assessment, one size may not fit all. Considerations of placement selection between PD and stroke, where different colored signals represent examples of different sensing axes of acceleration. Top: use of a single IMU on the lower back is useful in early PD to detect, e.g., gait variability, but a single IMU may not be fit for purpose as gait worsens. Accordingly, use of two IMUs on the legs may be more appropriate to detect slower, shuffling gait often seen in advanced PD. Bottom: a contrasting example, IMUs placed on the legs of a recent stroke survivor may be more optimal for gait assessment compared to the lower back. As gait for this person improves though a rehabilitation program, use of a single IMU may be more optimal and pragmatic.

Figure 2

Considerations of remotely assessing gait within an IoT context, demonstrating (i) a collection of IoT-enabled wearable and deployable devices streaming to an edge computing instance, e.g., smartphone; (ii) data being transmitted through wireless infrastructure from edge computing devices; and (iii) receiving data within a cloud instance for processing and storage.

Figure 3

A proposed infrastructure for multi-modal gait assessment in habitual environments. Here, (i) is informed by a computer vision-based system to provide environmental context; a low-cost wearable IMU streams inertial data to a (ii) smartphone, where preprocessing takes place before transmission to (iii) the cloud for storage and further analysis/assessment.