Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains. This usually requires capturing and consolidating data from multiple sources. As datasets do not necessarily originate from identical sensors, fused data typically results in a complex data problem. Because military is investigating how heterogeneous IoT devices can aid processes and tasks, we investigate a multi-sensor approach. Moreover, we propose a signal to image encoding approach to transform information (signal) to integrate (fuse) data from IoT wearable devices to an image which is invertible and easier to visualize supporting decision making. Furthermore, we investigate the challenge of enabling an intelligent identification and detection operation and demonstrate the feasibility of the proposed Deep Learning and Anomaly Detection models that can support future application that utilizes hand gesture data from wearable devices.

翻译：在公共和政府领域,对物(IoT)装置和数据融合技术的互联网部署越来越受欢迎,这通常需要从多种来源获取和整合数据。由于数据集不一定来自相同的传感器,集成数据通常会导致复杂的数据问题。由于军方正在调查各种IoT装置如何帮助进程和任务,我们调查一种多传感器方法。此外,我们提议采用图像编码方法信号,将信息(信号)从IoT可穿戴装置中的数据转换成不可忽略和易于视觉化的图像,以支持决策。此外,我们调查使智能识别和检测操作成为可能的挑战,并展示拟议中的深度学习和异常探测模型的可行性,这些模型能够支持未来应用,利用可磨损装置中的手势数据。