Recently published research [1] showed the potential of using brain-spine interfaces in restoring gait after a spinal cord injury in non-human primates. There, a closed loop, which consists of brain activity recording, spinal cord stimulation and optical gait tracking, was used in order to restore natural movement of a paralyzed lower limb. Within this closed loop, tracking the gait pattern was crucial for analyzing gait kinematics (Fig. 1) and providing a feedback to the stimulation device, in order to calibrate the stimulation signal. However, the optical system used for gait tracking consisted of several cameras and 2D motion trackers, which constrained its application to a laboratory environment, and did not allow a free movement in 3D space. To provide an alternative to optical motion tracking, we are developing a motion tracking method based on inertial motion sensors implanted directly in the limbs. Unlike their wearable counterparts, implanted sensors do not cause any kind of limitations or discomfort.
In the scope of the proposed project, the student will develop algorithms for tracking the motion of limbs, based on accelerometer and gyroscope data collected by the inertial sensors (Fig. 2). Having in mind that the nature of movements differs in lower and upper limbs, two algorithms will be developed, one for tracking the gait patterns, the second one for tracking upper limb movements (e.g., grasping). To honour the timing constraints in the movement restauration closed loop, both of the developed algorithms need to run in real time, with a latency of up to few tens of milliseconds. Both algorithms will be validated against the Vicon optical motion capture system[2].
**Requirements:**
Data processing skills are required. Hardware experience is a plus.
**References:**
[1] Capogrosso et al, “A brain–spine interface alleviating gait deficits after spinal cord injury in primates”, Nature Methods, 2016.
[2] https://www.vicon.com/
Recently published research [1] showed the potential of using brain-spine interfaces in restoring gait after a spinal cord injury in non-human primates. There, a closed loop, which consists of brain activity recording, spinal cord stimulation and optical gait tracking, was used in order to restore natural movement of a paralyzed lower limb. Within this closed loop, tracking the gait pattern was crucial for analyzing gait kinematics (Fig. 1) and providing a feedback to the stimulation device, in order to calibrate the stimulation signal. However, the optical system used for gait tracking consisted of several cameras and 2D motion trackers, which constrained its application to a laboratory environment, and did not allow a free movement in 3D space. To provide an alternative to optical motion tracking, we are developing a motion tracking method based on inertial motion sensors implanted directly in the limbs. Unlike their wearable counterparts, implanted sensors do not cause any kind of limitations or discomfort. In the scope of the proposed project, the student will develop algorithms for tracking the motion of limbs, based on accelerometer and gyroscope data collected by the inertial sensors (Fig. 2). Having in mind that the nature of movements differs in lower and upper limbs, two algorithms will be developed, one for tracking the gait patterns, the second one for tracking upper limb movements (e.g., grasping). To honour the timing constraints in the movement restauration closed loop, both of the developed algorithms need to run in real time, with a latency of up to few tens of milliseconds. Both algorithms will be validated against the Vicon optical motion capture system[2].
**Requirements:** Data processing skills are required. Hardware experience is a plus.
**References:** [1] Capogrosso et al, “A brain–spine interface alleviating gait deficits after spinal cord injury in primates”, Nature Methods, 2016.
[2] https://www.vicon.com/
Development and validation of algorithms for motion tracking using implanted inertial sensors
Development and validation of algorithms for motion tracking using implanted inertial sensors