Model-Based Approach for Cyber-Physical Systems Applications Development

Design and development of Cyber-Physical systems (CPS) are challenging due to their computational and physical dynamics. However, while studies investigated on model-driven approaches in other information system domains, research concerning how to support CPS design and development using modelling approaches and tools, is limited. Our research shows how model-based approaches and tools can be used to model scenarios in CPS application development while ensuring the CPS dynamism remains intact. We present a model followed by a prototype as an artefact to show a CPS design for health related monitoring. The paper introduces AutoWheel, an automatic wheelchair based monitoring system, as a case study for our design. The proposed design focuses on modeling the system and verifying the behavior of its working in the given mobility related health scenarios. The motivation of the AutoWheel project arises from the need for building low cost manageable technological interface and information system especially for the people in developing countries

Neuroergonomic Assessment of Wheelchair Control Using Mobile fNIRS

For over two centuries, the wheelchair has been one of the most common assistive devices for individuals with locomotor impairments without many modifications. Wheelchair control is a complex motor task that increases both the physical and cognitive workload. New wheelchair interfaces, including Power Assisted devices, can further augment users by reducing the required physical effort, however little is known on the mental effort implications. In this study, we adopted a neuroergonomic approach utilizing mobile and wireless functional near infrared spectroscopy (fNIRS) based brain monitoring of physically active participants. 48 volunteers (30 novice and 18 experienced) self-propelled on a wheelchair with and without a PowerAssist interface in both simple and complex realistic environments. Results indicated that as expected, the complex more difficult environment led to lower task performance complemented by higher prefrontal cortex activity compared to the simple environment. The use of the PowerAssist feature had significantly lower brain activation compared to traditional manual control only for novices. Expertise led to a lower brain activation pattern within the middle frontal gyrus, complemented by performance metrics that involve lower cognitive workload. Results here confirm the potential of the Neuroergonomic approach and that direct neural activity measures can complement and enhance task performance metrics. We conclude that the cognitive workload benefits of PowerAssist are more directed to new users and difficult settings. The approach demonstrated here can be utilized in future studies to enable greater personalization and understanding of mobility interfaces within real-world dynamic environments