SynLS: A novel diffusion-transformer framework for generating high-quality wearable sensor time series data to enhance health monitoring

Abstract

While global medical research is poised to benefit from the rapid advance of artificial intelligence (AI) technologies, veterinary medicine research often faces significant limitations due to data scarcity and availability issues. To address this issue, we proposed a generative modeling framework, SynLS, for generating highly realistic synthetic wearable sensor data. Leveraging diffusion architecture and transformer encoder mechanism, SynLS addressed the intricate challenges posed by these real-world wearable sensor data, including varied length, multiple dimensions, high diversity, high noise, periodicity, and trend. We have validated SynLS on four publicly-available livestock wearables databases with records for three health events (calving, estrus and diseases), and demonstrated its ablility in producing high-fidelity wearable sensor data, which could improve the downstream health events prediction tasks by 18.5% and 26.8% under two evaluation scenarios based on instance and timestamp, respectively. Additionally, introducting raw tri-axial accelerometer databases collected from animals and human further demonstrated extensibility of our framework, significantly enhancing downstream behavior classification tasks by 38.8% and 83.8%, respectively. The technical framework proposed in this work offers a potential generalized solution for data supplementation in wearables sensor databases, with potential applicability across veterinary medicine and other medical domains facing resource constraints.