Biblio

State-of-the-art approaches in gait analysis usually rely on one isolated tracking system, generating insufficient data for complex use cases such as sports, rehabilitation, and MedTech. We address the opportunity to comprehensively understand human motion by a novel data model combining several motion-tracking methods. The model aggregates pose estimation by captured videos and EMG and EIT sensor data synchronously to gain insights into muscle activities. Our demonstration with biceps curl and sitting/standing pose generates time-synchronous data and delivers insights into our experiment’s usability, advantages, and challenges.

Fulcrum decoders can trade off between computational complexity and the number of received packets. This allows heterogeneous nodes to decode at different level of complexity in accordance with their computing power. Variations of Fulcrum codes, like dynamic sparsity and expansion packets (DSEP) have significantly reduced the encoders and decoders' complexity by using dynamic sparsity and expansion packets. However, limited effort had been done for recoders of Fulcrum codes and their variations, limiting their full potential when being deployed at multi-hop networks. In this paper, we investigate the drawback of the conventional Fulcrum recoding and introduce a novel recoding scheme for the family of Fulcrum codes by limiting the buffer size, and thus memory needs. Our evaluations indicate that DSEP recoding mechamism increases the recoding goodput by 50%, and reduces the decoding overhead by 60%-90% while maintaining high decoding goodput at receivers and small memory usage at recoders compared with the conventional Fulcrum recoding. This further reduces the resources needed for Fulcrum codes at the recoders.