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Personalisation of assistance for a soft-robotic system
In this project you will acquire and process user data, implement state-of-the-art sensor fusion algorithms, and develop adaptive algorithms to deliver personalised assistance profiles with the soft-robotic exosuit.
Keywords: Machine Learning, Control Aglorithm, Personalisation, Robotic Therapy, Soft Robotics, Exosuit, Medical Robotics, Data Science, Sensor Fusion
Weakness of the musculoskeletal system can cause movement problems that decrease the quality of life of a person. This weakness may be the result of genetic disorders, a neurological condition or injury, or simply healthy ageing. Unfortunately, many of these conditions cannot be healed with the current state of medical care available. As a result, the best option to regain mobility is the use of assistive technologies. At ETH Zurich SpinOff MyoSwiss we are creating the Myosuit: a novel technology that combines robotics and functional textiles to provide users with an extra layer of wearable muscles to support their activities of daily life.
The unique quality of the Myosuit is its ability to assist across multiple pathologies with minimal adjustments in hardware. To provide the optimal assistance pattern, we need to identify the degree of deviation from the “standard” gait pattern. The aim of this project is to develop an algorithm that personalises the assistance pattern for the Myosuit for such activities of daily life as level walking, stair ascend / descend and sitting transfers.
Throughout this project you will acquire and process user data, implement state-of-the-art sensor fusion algorithms, and develop adaptive algorithms to deliver personalised assistance profiles with the Myosuit.
Weakness of the musculoskeletal system can cause movement problems that decrease the quality of life of a person. This weakness may be the result of genetic disorders, a neurological condition or injury, or simply healthy ageing. Unfortunately, many of these conditions cannot be healed with the current state of medical care available. As a result, the best option to regain mobility is the use of assistive technologies. At ETH Zurich SpinOff MyoSwiss we are creating the Myosuit: a novel technology that combines robotics and functional textiles to provide users with an extra layer of wearable muscles to support their activities of daily life.
The unique quality of the Myosuit is its ability to assist across multiple pathologies with minimal adjustments in hardware. To provide the optimal assistance pattern, we need to identify the degree of deviation from the “standard” gait pattern. The aim of this project is to develop an algorithm that personalises the assistance pattern for the Myosuit for such activities of daily life as level walking, stair ascend / descend and sitting transfers.
Throughout this project you will acquire and process user data, implement state-of-the-art sensor fusion algorithms, and develop adaptive algorithms to deliver personalised assistance profiles with the Myosuit.
1. Literature research about the biomechanical changes due to the relevant disabilities
2. Patient data acquisition and processing
3. Gait data analysis and parameterisation
4. Implementation of sensor fusion / learning algorithms
1. Literature research about the biomechanical changes due to the relevant disabilities 2. Patient data acquisition and processing 3. Gait data analysis and parameterisation 4. Implementation of sensor fusion / learning algorithms
Not specified
1. Interest for interdisciplinary sciences, in particular: biomechanics, data science and learning
2. Familiarity with Python (advantage)
1. Interest for interdisciplinary sciences, in particular: biomechanics, data science and learning 2. Familiarity with Python (advantage)
Gleb Koginov (gleb@myoswiss.com), MyoSwiss AG
Kai Schmidt (kai@myoswiss.com), MyoSwiss AG
Jaime Duarte (jaime.duarte@myoswiss.com), MyoSwiss AG / ETH Zurich
Gleb Koginov (gleb@myoswiss.com), MyoSwiss AG
Kai Schmidt (kai@myoswiss.com), MyoSwiss AG
Jaime Duarte (jaime.duarte@myoswiss.com), MyoSwiss AG / ETH Zurich