ISLAB/CAISR - User contributions [en]

Gait analysis using wearable sensors in Parkinson's disease

2019-09-30T14:28:49Z

Taha: Created page with "{{StudentProjectTemplate |Summary=The project aims to develop a machine learning tool for the assessment of Parkinsonian gait in a natural environment |Keywords=Signal process..."

{{StudentProjectTemplate
|Summary=The project aims to develop a machine learning tool for the assessment of Parkinsonian gait in a natural environment
|Keywords=Signal processing, Machine Learning, Parkinson's disease
|TimeFrame=Autumn 2019 - Spring 2020
|Supervisor=Taha Khan
|Examiner=Fernando Alonso-Fernandez
|Level=Master
|Status=Open
}}
Gait impairment is an important symptom in Parkinson's disease. The project aims to investigate if wearables such as inertial sensors could be used to perform long-term continuous monitoring of Parkinsonian gait in a natural environment. Data consist of time-series of accelerometer readings and pressure insole sensors recorded from Parkinson patients during their assessment of gait by a doctor at a hospital in Johannesburg South Africa using a standard clinical protocol, as well as, during their walks around the clinic without the protocol in a natural setting. The project will explore and develop novel methods and gait features that could be extracted from the time-series and used for training machine learning models for automatic classification and monitoring of the severity of gait impairment in a natural environment.

A study about the future and usability of Bio-electronic “smart” implants in humans

2018-10-18T15:02:09Z

Taha:

{{StudentProjectTemplate
|Summary=Feasibility study on bioimplants in humans.
|Keywords=Biohack, wetware, health, diagnosis, payment, credit card, nfc, mifare
|TimeFrame=Spring 2019
|Prerequisites=Experience with programming, electronics, NFC/Mifare and other wireless protocols and
sensor wearables. Interest and experience in Biohacking and wetware is also good.
|Supervisor=Taha Khan, Stefan Nordlander
|Level=Master
|Status=Open
}}
We would like to gain an understanding of the general knowledge:understanding:need: attitude regarding active and passive electronic
implants. Primarily for science, research and medical purposes. But in the long term cosmetical and practical as well. We already have MiFare/NFC implants to open doors and pay for coffee. But there’s a lot of controversy surrounding this. What are some of
the biggest challenges before we take the next step?

Speaker/speech recognition and engagement

2018-10-18T15:01:22Z

Taha: Created page with "{{StudentProjectTemplate |Summary=Algorithms, sensors and devices for recognizing speaker as well as spoken words and engagement from an arbitrary number of people in a conver..."

{{StudentProjectTemplate
|Summary=Algorithms, sensors and devices for recognizing speaker as well as spoken words and engagement from an arbitrary number of people in a conversation
|Keywords=Machine learning, speaker recognition, speech recognition, speaker engagement
|TimeFrame=Spring 2019
|Prerequisites=Speech analysis, sensors, electronics, programming
|Supervisor=Taha Khan, Oskar Lundgren
|Level=Master
|Status=Open
}}

Contactless monitoring of blood pressure using photoplethysmography

2018-10-18T14:54:04Z

Taha:

{{StudentProjectTemplate
|Summary=A camera-based system for non-invasive monitoring of blood pressure
|Keywords=Photoplethysmography, blood pressure, computer vision, neural networks
|TimeFrame=Fall 2018
|Prerequisites=Computer vision or digital image processing, digital signal processing, Machine learning, Artificial Intelligence
|Supervisor=Taha Khan
|Level=Master
|Status=Open
}}
In emergency medicine and urgent care, the first assessment and triage are crucial to examine the severity of disease, condition or injury, for prioritizing tests and procedures. Vital signs are fundamental in this process and are investigated in all validated clinical assessment methods. This proposal aims to develop a camera-based system based on photoplethysmography to enable non-contact scanning of vital signs, specifically pulse rate and blood pressure (BP), replacing the conventional setup of devices that are time-consuming and require physical contact. The method will work by recording a video for a few seconds using a high-speed RGB camera. This video will then be processed by image and signal processing algorithms, and machine learning to classify blood pressure, and trigger a flag in case if a patient is in need of emergency care, enabling the staff in the care unit to decide on the level of caution to be taken.

Research Question: How to classify blood pressure levels using photoplethysmography and machine learning?

A study about the future and usability of Bio-electronic “smart” implants in humans

2018-10-18T14:50:19Z

Taha:

{{StudentProjectTemplate
|Summary=Feasibility study on bioimplants in humans.
|Keywords=Biohack, wetware, health, diagnosis, payment, credit card, nfc, mifare
|TimeFrame=Spring 2018
|References=MBP0010
|Prerequisites=Experience with programming, electronics, NFC/Mifare and other wireless protocols and
sensor wearables. Interest and experience in Biohacking and wetware is also good.
|Supervisor=Taha Khan, Stefan Nordlander
|Level=Master
|Status=Open
}}
We would like to gain an understanding of the general knowledge:understanding:need: attitude regarding active and passive electronic
implants. Primarily for science, research and medical purposes. But in the long term cosmetical and practical as well. We already have MiFare/NFC implants to open doors and pay for coffee. But there’s a lot of controversy surrounding this. What are some of
the biggest challenges before we take the next step?

A study about the future and usability of Bio-electronic “smart” implants in humans

2018-10-18T14:43:31Z

Taha: Created page with "{{StudentProjectTemplate |Summary=A study about the future and usability of Bio-electronic “smart” implants in humans. |Supervisor=Taha Khan }} We would like to gain an un..."

{{StudentProjectTemplate
|Summary=A study about the future and usability of Bio-electronic “smart” implants in humans.
|Supervisor=Taha Khan
}}
We would like to gain an understanding of the general knowledge:understanding:need: attitude regarding active and passive electronic
implants. Primarily for science, research and medical purposes. But in the long term cosmetical and practical as well. We already have MiFare/NFC implants to open doors and pay for coffee. But there’s a lot of controversy surrounding this. What are some of
the biggest challenges before we take the next step?

File:Head-unknown.jpg

2018-10-04T10:57:17Z

Taha: Taha uploaded a new version of "File:Head-unknown.jpg"

Non-contact estimation of blood pressure using photoplethysmography

2018-10-02T10:41:30Z

Taha: Created page with "{{StudentProjectTemplate |Summary=The proposal aims to develop a camera-based system for estimating blood pressure using machine learning and photoplethysmography |TimeFrame=O..."

{{StudentProjectTemplate
|Summary=The proposal aims to develop a camera-based system for estimating blood pressure using machine learning and photoplethysmography
|TimeFrame=October 2018 to July 2019
|Supervisor=Taha Khan,
}}
In emergency medicine and urgent care, the first assessment and triage are crucial to examine the severity of disease, condition or injury, for prioritizing tests and procedures. Vital signs are fundamental in this process and are investigated in all validated clinical assessment methods. This proposal aims to develop a camera-based system based on photoplethysmography enabling non-contact scanning of vital signs, specifically pulse rate and blood pressure (BP), replacing the conventional setup of devices that is time-consuming to operate and requires physical contact. The proposed solution will work by recording a video for a few seconds using a high-speed RGB camera. This video will then be processed by image and signal processing algorithms, and machine learning for categorizing levels of blood pressure and risk classes, to trigger a flag in case if a patient is in need of emergency care, enabling the staff in the care unit to decide on the level of caution to be taken.

Research Question: How to classify blood pressure levels using photoplethysmography and machine learning?

Contactless monitoring of blood pressure using photoplethysmography

2018-10-01T21:59:23Z

Taha: Created page with "{{StudentProjectTemplate |Summary=A camera-based system for non-invasive monitoring of blood pressure |Supervisor=Taha Khan }} In emergency medicine and urgent care, the first..."

{{StudentProjectTemplate
|Summary=A camera-based system for non-invasive monitoring of blood pressure
|Supervisor=Taha Khan
}}
In emergency medicine and urgent care, the first assessment and triage are crucial to examine the severity of disease, condition or injury, for prioritizing tests and procedures. Vital signs are fundamental in this process and are investigated in all validated clinical assessment methods. This proposal aims to develop a camera-based system based on photoplethysmography to enable non-contact scanning of vital signs, specifically pulse rate and blood pressure (BP), replacing the conventional setup of devices that are time-consuming and require physical contact. The method will work by recording a video for a few seconds using a high-speed RGB camera. This video will then be processed by image and signal processing algorithms, and machine learning to classify blood pressure, and trigger a flag in case if a patient is in need of emergency care, enabling the staff in the care unit to decide on the level of caution to be taken.

Research Question: How to classify blood pressure levels using photoplethysmography and machine learning?