Simão Carvalho- Phd Student
- PhD Student at University of Minho
Simão Carvalho
- Phd Student
- PhD Student at University of Minho
About
9
Publications
1,264
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Introduction
I am a Biomedical Engineer, master’s in medical Electronics and PhD student in CMEMS, at BiRD lab(http://birdlab.dei.uminho.pt/), University of Minho. My Phd research focuses on the development and validation of a closed-loop, adaptative and bioinspired Control for Functional Electrical Stimulation aiming a personalized lower limb motor Rehabilitation and Assistance.
Skills and Expertise
Current institution
Publications
Publications (9)
Exoskeletons can assist human locomotion in real-life scenarios, but existing tools for decoding locomotion modes (LMs) focus on recognition rather than prediction, which can lead to delayed assistance. This study proposes a long short-term memory (LSTM) neural network to predict five LMs (level-walking, ramp ascent/descent, stair ascent/descent) w...
The use of functional electrical stimulation (FES) through neuroprosthesis is becoming a promising solution in lower limb neurorehabilitation. However, the wearability constraints and time-consuming tuning of stimulation parameters still limit the daily use of neuroprostheses. This work proposes two major contributions, namely: (i) a conceptual des...
Neurological diseases may reduce Tibialis Anterior (TA) muscle recruitment capacity causing gait disorders, such as drop foot (DF). The majority of DF patients still retain excitable nerves and muscles which makes Functional Electrical Stimulation (FES) an adequate technique to restore lost mobility. Recent studies suggest the need for developing p...
This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low...
More versatile, user-independent tools for recognizing and predicting locomotion modes (LMs) and LM transitions (LMTs) in natural gaits are still needed. This study tackles these challenges by proposing an automatic, user-independent recognition and prediction tool using easily wearable kinematic motion sensors for innovatively classifying several...
Current research suggests the emergent need to recognize and predict locomotion modes (LMs) and LM transitions to allow a natural and smooth response of lower limb active assistive devices such as prostheses and orthosis for daily life locomotion assistance. This Master dissertation proposes an automatic and user-independent recognition and predict...
