Difference between revisions of "Smart Patch For Heath Care And Rehabilitation"
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==Short Description== | ==Short Description== | ||
| − | + | The Smart Patch project will design autonomous, low power and mesh enabled multi-sensor wearable smart patches. They will be based on the always-on smart sensing paradigm to continuously acquire process and stream physiological data in real-time. They can be trained to autonomously detect illness symptoms or other physical conditions, such as stress. This project will be done in collaboration with IBM Research in Zurich and Lausanne University Hospital and Neural Control of Movement in the Department of Health Sciences and Technology (D-HEST prof Nicole Wenderoth) . Bachelor’s and Master’s students are invited to design the low power smart patches that can be attached to the human body. The data can be processed on-board and/or sent to a gateway, for instance to the IBM servers, to perform further data analysis. | |
| + | Students will develop and build a product from the beginning. Together in a team, they will learn how to structure problems and identify solutions, system analysis, and simulation, as well as presentation and documentation techniques. They will get access to D-ITET labs and state-of-the-art engineering tools (Matlab, Simulink, Firmware development IDE, PCB Design etc.) as well as to sensors that are unique in the world provided as early access by collaborative companies (such as STMicroelectronics, Sony, Infineon, FLIR, Nordic, among others.) | ||
| + | |||
| + | Three application scenarios: | ||
| + | |||
| + | * Monitoring of firefighters | ||
| + | IBM Research measured the heart rate variability (HRV) with wearable devices in realistic training environments for firefighters, who were subject to physical and psychological stress. Using machine learning algorithms, different stress types were | ||
| + | identified. These predictions are used to help firefighters train more efficiently and experience personal limits, help coordinators to put together the right team for specific missions, and finally help mission commanders keep their teams safe. | ||
| + | |||
| + | * Healthcare | ||
| + | IBM’s current challenge for clinical implementation is the manual measurement of vital parameters, which increases workload and puts medical staff in direct contact with infective patients. Smart patches can help both medical staff and elderly people to improve the quality of their work and life. The smart patch should stay on the human body for long periods of time and be able to perform both processing and communication with external | ||
| + | gateways. This project will involve the real field test with Lausanne University Hospital. | ||
| + | |||
| + | * Rehabilitation (collaboration with D-HEST). | ||
| + | Application Scenario | ||
| + | The Neural Control of Movement Lab is developing neurofeedback technology for patients with neurological impairments. One aim is to develop new, user-friendly interfaces for decoding motor intentions from weak muscle activity of stroke patients. This information is used to control wearable technologies with embedded clinical intelligence for providing rehabilitation training. In addition to rehabilitation, the signals we measure can be used for implementing novel human-machine control schemes. | ||
| + | Goal & Tasks | ||
| + | The main goal is to design and develop a wearable device ready for in-field measurements in our lab or with stroke patients in the clinic. The student(s) will also work to develop firmware that provides the needed functionality, including signal processing and a wireless communication protocol. According to the student level and the thesis was undertaken (Bachelor/semester/master), the final task description will be assigned. | ||
| + | |||
| + | |||
===Goal & Tasks=== | ===Goal & Tasks=== | ||
| − | The project(s) will address the following challenges: | + | The project(s) will address the following challenges (not all are for a single project): |
| − | * | + | * Design and develop smart wearable devices using novel sensors, processors, and wireless interfaces. |
| + | * dataset acquisitions for machine learning and eventually investigate on machine learning. | ||
| + | * Indoor and outdoor localization and tracking | ||
* The algorithms will be evaluated and optimized for the capability of the processors to increase the energy efficiency and, at the same increase the response time of the detection | * The algorithms will be evaluated and optimized for the capability of the processors to increase the energy efficiency and, at the same increase the response time of the detection | ||
| − | * A complete hardware and software prototype of drones and smart | + | * A complete hardware and software prototype of drones and smart wearable device, which includes all the subsystems (sensor acquisition, preprocessing, and processing and radio communication), will be developed to demonstrate the benefits of the proposed approach and the capability to achieve low latency and energy efficiency on the challenging scenario of autonomous drones. |
| − | * Indoor Localization using | + | * Indoor Localization using Ultrawideband and unique wake up radio designed in ITET will be used and evaluated by the students. |
* The working prototype with the ARM Cortex-M processors and novel sensors will be evaluated. | * The working prototype with the ARM Cortex-M processors and novel sensors will be evaluated. | ||
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* Knowledge of embedded systems | * Knowledge of embedded systems | ||
* Basic Knowledge or motivation to learn machine learning and signal processing | * Basic Knowledge or motivation to learn machine learning and signal processing | ||
| − | * Motivation to learn how work with sensors provided by companies and even not yet on the market | + | * Motivation to learn how to work with sensors provided by companies and even not yet on the market |
* Motivation to build and test a real system and acquiring field data | * Motivation to build and test a real system and acquiring field data | ||
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===Status: Available === | ===Status: Available === | ||
* Looking for Bachelor Thesis, Semester and Master Project Students | * Looking for Bachelor Thesis, Semester and Master Project Students | ||
| − | : Supervisors: [[:User:magnom|Michele Magno]], Tommaso Polonelli tommaso.polonelli@pbl.ee.ethz.ch | + | : Supervisors: [[:User:magnom|Michele Magno]], Tommaso Polonelli tommaso.polonelli@pbl.ee.ethz.ch, Christian Vogt <christian.vogt@pbl.ee.ethz.ch>, Rieder Michael <michael.rieder@pbl.ee.ethz.ch> |
Latest revision as of 16:24, 30 November 2020
Contents
Short Description
The Smart Patch project will design autonomous, low power and mesh enabled multi-sensor wearable smart patches. They will be based on the always-on smart sensing paradigm to continuously acquire process and stream physiological data in real-time. They can be trained to autonomously detect illness symptoms or other physical conditions, such as stress. This project will be done in collaboration with IBM Research in Zurich and Lausanne University Hospital and Neural Control of Movement in the Department of Health Sciences and Technology (D-HEST prof Nicole Wenderoth) . Bachelor’s and Master’s students are invited to design the low power smart patches that can be attached to the human body. The data can be processed on-board and/or sent to a gateway, for instance to the IBM servers, to perform further data analysis. Students will develop and build a product from the beginning. Together in a team, they will learn how to structure problems and identify solutions, system analysis, and simulation, as well as presentation and documentation techniques. They will get access to D-ITET labs and state-of-the-art engineering tools (Matlab, Simulink, Firmware development IDE, PCB Design etc.) as well as to sensors that are unique in the world provided as early access by collaborative companies (such as STMicroelectronics, Sony, Infineon, FLIR, Nordic, among others.)
Three application scenarios:
- Monitoring of firefighters
IBM Research measured the heart rate variability (HRV) with wearable devices in realistic training environments for firefighters, who were subject to physical and psychological stress. Using machine learning algorithms, different stress types were identified. These predictions are used to help firefighters train more efficiently and experience personal limits, help coordinators to put together the right team for specific missions, and finally help mission commanders keep their teams safe.
* Healthcare
IBM’s current challenge for clinical implementation is the manual measurement of vital parameters, which increases workload and puts medical staff in direct contact with infective patients. Smart patches can help both medical staff and elderly people to improve the quality of their work and life. The smart patch should stay on the human body for long periods of time and be able to perform both processing and communication with external gateways. This project will involve the real field test with Lausanne University Hospital.
- Rehabilitation (collaboration with D-HEST).
Application Scenario The Neural Control of Movement Lab is developing neurofeedback technology for patients with neurological impairments. One aim is to develop new, user-friendly interfaces for decoding motor intentions from weak muscle activity of stroke patients. This information is used to control wearable technologies with embedded clinical intelligence for providing rehabilitation training. In addition to rehabilitation, the signals we measure can be used for implementing novel human-machine control schemes. Goal & Tasks The main goal is to design and develop a wearable device ready for in-field measurements in our lab or with stroke patients in the clinic. The student(s) will also work to develop firmware that provides the needed functionality, including signal processing and a wireless communication protocol. According to the student level and the thesis was undertaken (Bachelor/semester/master), the final task description will be assigned.
Goal & Tasks
The project(s) will address the following challenges (not all are for a single project):
- Design and develop smart wearable devices using novel sensors, processors, and wireless interfaces.
- dataset acquisitions for machine learning and eventually investigate on machine learning.
- Indoor and outdoor localization and tracking
- The algorithms will be evaluated and optimized for the capability of the processors to increase the energy efficiency and, at the same increase the response time of the detection
- A complete hardware and software prototype of drones and smart wearable device, which includes all the subsystems (sensor acquisition, preprocessing, and processing and radio communication), will be developed to demonstrate the benefits of the proposed approach and the capability to achieve low latency and energy efficiency on the challenging scenario of autonomous drones.
- Indoor Localization using Ultrawideband and unique wake up radio designed in ITET will be used and evaluated by the students.
- The working prototype with the ARM Cortex-M processors and novel sensors will be evaluated.
Prerequisites
(not all need to be met by the single candidate)
- Knowledge of high and low-level programming languages (e.g. Python, embedded C)
- Knowledge of embedded systems
- Basic Knowledge or motivation to learn machine learning and signal processing
- Motivation to learn how to work with sensors provided by companies and even not yet on the market
- Motivation to build and test a real system and acquiring field data
Detailed Task Description
A detailed task description will be worked out right before the project, taking the student's interests and capabilities into account.
Status: Available
- Looking for Bachelor Thesis, Semester and Master Project Students
- Supervisors: Michele Magno, Tommaso Polonelli tommaso.polonelli@pbl.ee.ethz.ch, Christian Vogt <christian.vogt@pbl.ee.ethz.ch>, Rieder Michael <michael.rieder@pbl.ee.ethz.ch>
Character
- 35% Theory and Algorithms
- 35% Implementation w
- 30% Data acquisition, Verification, and Testing
