*2.3.2 Deployment of VIGOR on affordable hardware using edge computing*

Edge computing enables real-time knowledge generation and application to occur at the source of the data close to user device [54, 55], which makes it particularly suitable for the proposed latency-sensitive system. An edge server can be adapted to serve multiple users through interaction with their devices. There are communication and computing trade-offs between the edge server and each user device. Data could either be locally processed at the user device or else be transmitted to and processed at the edge server. Different strategies introduce different communication costs, resulting in different delay performance. To provide the best quality of experience for users, the following tasks are involved: (1) Identification and modularization of computing tasks: the computing tasks of data preprocessing, kinetic movement recognition, and individualization of movement choreography need to be identified and the corresponding computing overheads (CPU cycles, memory) need to be determined. (2) Design, prototyping and enhancement of offloading schemes: Based on the results of bandwidth and delay analysis as well as delay performance requirement, computation offloading schemes need to be developed to determine which computing tasks should be performed locally at the user device and which computation tasks should be offloaded to the edge server. As shown in **Figure 4**, an illustrative concept demonstration about edge-computing-enabled VIGOR is given in our online video [56].

**Figure 4.** *Edge-computing-enabled VIGOR deployed on commodity hardware (demo in online video [56]).*
