181 citations · 453 across the 25 of their papers we have counts for
7 papers · 1 filter
From Biological Synapses to Intelligent Robots
Birgitta Dresp-Langley
This review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is…
Making Sense of Complex Sensor Data Streams
Rongrong Liu, Birgitta Dresp-Langley
This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non dominant…
Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
Rongrong Liu, Florent Nageotte, Philippe Zanne +2
Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has…
From hand to brain and back: Grip forces deliver insight into the functional plasticity of somatosensory processes
Birgitta Dresp-Langley
The human somatosensory cortex is intimately linked to other central brain functions such as vision, audition, mechanoreception, and motor planning and control. These links are est…
Wearable Sensors for Spatio-Temporal Grip Force Profiling
Rongrong Liu, Florent Nageotte, Philippe Zanne +2
Wearable biosensor technology enables real-time, convenient, and continuous monitoring of users behavioral signals. Such include signals relative to body motion, body temperature,…
Correlating grip force signals from multiple sensors highlights prehensile control strategies in a complex task-user system
Birgitta Dresp-Langley, Florent Nageotte, Philippe Zanne +1
Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is gene…