Neuromotor Systems Laboratory
 
 
Research Updates
   
     
 

Custom PC Build

 
 

We were happy to have the chance to nerd out and build a wicked fast PC to run our motion capture system and analyze data. We are using the Ryzen 9 5900X CPU which nominally runs at 3.7 GHz, but we were able to overclock it to a clock speed of 4.95 MHz (awesome). AMD's Precision Boost Overdrive seems pretty useful; it dynamically overclocks the CPU when its algorithm decides conditions are right (e.g., temperature, workload, and current draw are OK). We also went RGB so we can be mesmerized by the pretty colors.

 
 

Motherboard: X570 Aorus Xtreme
Processor: AMD Ryzen 9 5900X (12-Core; 3.7 GHz)
Memory: Corsair Vengeance RGB Pro 2 x 8 GB (DDR4 3600)
Cooling: Kraken X73 RGB (360mm Liquid Cooler)
Power: Thermaltake Toughpower 850E (80+ Platinum)
Storage: WD_Black 1 TB SN850 NVMe (Gen4 PCIe, M2)
Graphics: Nvidia Quadro K4200 (waiting for better availability!)
Case: Lian Li 011Dynamic XL

 

 
     
 

Walker Blinding

 
 

How do we keep the identity of human trainers hidden from subjects while they walk on a treadmill?

 
 

It might look silly, but it is effective. For our current study we wanted to hide the identity of experimenters from someone walking on the treadmill and prevent the walkers from seeing what is on the ground below them. Our solution was a giant spandex sheet with a hole, through which the subjects poke their heads through. A bounus is that this also keeps CJ from scratching his head. There is an additional a vertical sheet on the side of the walker that is not shown in the image.

 
     
 

Virtual Patient Simulator

 
 

Our lab group developed a novel virtual patient (VP) simulator. The simulator allows a trainerl to physically assist a virtual stroke survivor (i.e., a VP) to improve its locomotor characteristics as it walks on a virtual treadmill. Since the physical dynamics of the patient are mathematically specified, the variance observed in trainer performance can be precisely attributed to experimental manipulations. Thus far, we have used the simulator to show that humans learn internal models of patient locomotor dynamics that adapt to exploit the natural physics (Hasson and Goodman, 2019). We have also demonstrated that the process of learning the locomotor dynamics of a patient does not benefit from being able to see a patient’s body segments (Hasson and Jalili, 2019).

 
     
 

Cyberphysical Augmentation of Therapists

 
 

Building from the above work, we lead an interdisciplinary team of investigators from Northeastern and Tufts Medical Center on an NIH-funded project with the long-term goal of improving neuroplasticity and gait training outcomes in chronic stroke survivors. This project replaces the virtual stroke survivors (VP) with real ones in an approach called Cyberphysical Augmentation (CA). To keep the physics of the interaction similar (i.e., end-point interaction), we were the first to successfully adapt a robotic arm for gait training (Franchi et al. 2015). In the CA approach, a therapist provides assistance by applying light forces to a small manipulandum, which are amplified and transferred to the patient by a large robotic arm.

 
 
 
   
     
     
     
© 2022 Christopher J. Hasson