Professor Grigore Burdea is the lead author of the paper titled "Novel Therapeutic Game Controller for Telerehabilitation of Spastic Hands: Two Case Studies" that has won the best paper award at the 13th International Conference on Virtual Rehabilitation (ICVR) that was held in Tel Aviv, Israel in July 2019. The goal of the ICVR conference series is to provide an overview of applied and clinical research on technologies in the field of virtual rehabilitation. In this paper, Professor Burdea and his coauthors design a novel BrightBrainer Grasp (BBG) controller that can overcome challenges faced in post-stroke rehabilitation due to hand spasticity. The custom controller measures power grasp, finger extension, wrist position and orientation, as well as 3D hand position. It is designed to minimize friction when used by those with no gravity bearing. The paper presents a detailed description of the BBG controllers and their interaction with the BrightBrainer™ gaming system, including two successful case studies.

Congratulations on this achievement, Greg!

ECE Associate Professor Laleh Najafizadeh is the recipient of an exploratory EAGER NSF award for the project "Adapting Multi-Modal BCI-Based Assistive Technologies for Patients with High Spinal Cord Injury." This is a 1 year $120,000 award from the NSF.

In this project, Laleh will advance brain computer interface (BCI) technology by bringing the technology closer to the needs of patients with high spinal cord injury (SCI). While BCI-based assistive technologies hold a great promise to improve the quality of life of patients with SCI by enabling them to independently perform simple activities of their daily life, several factors have prevented their successful application in practical and clinical settings. A major contributing factor to this problem is related to the secondary health conditions (e.g. chronic pain) that are developed as a result of SCI, but have been largely ignored in BCI research. The presence of pain can negatively impact the control signals acquired from BCIs, and consequently, compromise the operation of the assistive device, thereby, adding to stress, discomfort, and depression for the patient. Using innovative approaches and experiments, this research aims to investigate how pain impacts the operation of multi-modal BCIs. The outcome of this work is expected to provide guidelines for developing effective methods that aid in realizing robust and high-performance BCI-based assistive technologies for SCI patients.

The project details can be found here:
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841087&HistoricalAwards=false

Congratulations, Laleh!

ECE Professor Yingying Chen is a recipient of a new National Science Foundation (NSF) award for the project titled "Software Hardware Architecture Co-design for Low-power Heterogeneous Edge Devices" This is a three-year project of $500,000 in collaboration with Binghamton University. The Rutgers share of the award is $320,000.

In this project, Yingying and her team aim to remove the barrier that keeps deep learning techniques away from pervasive low-power mobile edge computing devices and enable high-accuracy, low-latency applications in future mobile edge computing. This research plans to systematically investigate the fundamental and challenging issues targeting to significantly reduce the cost of deep learning inference process in mobile edge devices with guaranteed performance. The team will build a software accelerator that enables the broad deployment of heavy-cost deep learning models into resource-constrained, heterogeneous mobile edge devices (e.g., low-cost sensing platforms and IoT devices). They will develop deep-learning resource management algorithms that can adjust structures of different deep learning models according to hardware constraints of heterogeneous edge devices. More specifically, this research analyzes distinct deep learning behaviors on mobile edge devices and designs different strategies to improve the efficiency of multiple deep-learning-based inference models. Furthermore, this research develops algorithms that can adjust the complexity of different deep learning models to reduce their energy and memory consumption on mobile edge devices. In addition, this project designs power-centric resource reallocation algorithms to verify and deploy the mobile-friendly deep learning models.

You can find more details on the project at the NSF page here:

 https://nsf.gov/awardsearch/showAward?AWD_ID=1909963&HistoricalAwards=false

Congratulations, Yingying!