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Calibration Algorithm for Smart Neurostimulation Sock
The ETH spin-off MYNERVA is pioneering the development of a smart neurostimulation sock aimed at alleviating symptoms associated with neuropathic diseases of the lower limb, such as those caused by diabetic neuropathy. Through targeted transcutaneous electrical stimulation (TENS) of the nerves innervating the foot, MYNERVA reduces neuropathic pain and restores sensory perception below the foot in affected individuals, improving gait and balance.
More information on MYNERVA:
https://www.linkedin.com/company/mynervamedical/
https://www.wysszurich.ch/projects/mynerva
Keywords: Data Analysis, Data Processing, Machine Learning, Neurostimulation, TENS, Clinical Trials
**Background**
The ETH spin-off MYNERVA is pioneering the development of a smart neurostimulation sock aimed at alleviating symptoms associated with neuropathic diseases of the lower limb, such as those caused by diabetic neuropathy. Through targeted transcutaneous electrical stimulation (TENS) of the nerves innervating the foot, MYNERVA reduces neuropathic pain and restores sensory perception below the foot in affected individuals, improving gait and balance.
Empowered by a user-friendly mobile application, patients can independently regulate stimulation in real-time, eliminating the need for professional intervention. Incorporating the hardware into a sock facilitates seamless integration into patients' daily routines.
To ensure adaptability to varying patient anatomies, the electrode arrays and pressure sensors integrated into the sock require precise calibration before each use. Conventional calibration methods are excessively timeconsuming, rendering daily usage impractical. Engineering a fast and accurate calibration algorithm for both electrodes and pressure sensors based on subjective feedback is therefore a crucial component in the development of the device.
First prototypes of the described neurostimulation device have been developed previously and were validated through several clinical studies.
**Diabetic Neuropathy**
Patients suffering from Diabetic Peripheral Neuropathy (DPN) often experience severe damage to peripheral nerves, particularly in the lower limb, resulting in chronic pain and sensory loss. Traditional treatments such as opioids carry side effects, while physiotherapy primarily focuses on compensatory walking strategies, neglecting sensory restoration and gait improvement.
**TENS for Diabetic Neuropathy**
Transcutaneous Electrical Nerve Stimulation (TENS) offers promising prospects for addressing chronic pain and sensory loss in patients with DPN. Strategically positioned arrays of electrodes enable precise stimulation of nerve pathways in the foot, which facilitates pain reduction and can elicit somatotopic sensations. By synchronizing the latter with pressure sensor activation, sensory perceptions below the foot can be restored.
**Background**
The ETH spin-off MYNERVA is pioneering the development of a smart neurostimulation sock aimed at alleviating symptoms associated with neuropathic diseases of the lower limb, such as those caused by diabetic neuropathy. Through targeted transcutaneous electrical stimulation (TENS) of the nerves innervating the foot, MYNERVA reduces neuropathic pain and restores sensory perception below the foot in affected individuals, improving gait and balance.
Empowered by a user-friendly mobile application, patients can independently regulate stimulation in real-time, eliminating the need for professional intervention. Incorporating the hardware into a sock facilitates seamless integration into patients' daily routines.
To ensure adaptability to varying patient anatomies, the electrode arrays and pressure sensors integrated into the sock require precise calibration before each use. Conventional calibration methods are excessively timeconsuming, rendering daily usage impractical. Engineering a fast and accurate calibration algorithm for both electrodes and pressure sensors based on subjective feedback is therefore a crucial component in the development of the device.
First prototypes of the described neurostimulation device have been developed previously and were validated through several clinical studies.
**Diabetic Neuropathy**
Patients suffering from Diabetic Peripheral Neuropathy (DPN) often experience severe damage to peripheral nerves, particularly in the lower limb, resulting in chronic pain and sensory loss. Traditional treatments such as opioids carry side effects, while physiotherapy primarily focuses on compensatory walking strategies, neglecting sensory restoration and gait improvement.
**TENS for Diabetic Neuropathy**
Transcutaneous Electrical Nerve Stimulation (TENS) offers promising prospects for addressing chronic pain and sensory loss in patients with DPN. Strategically positioned arrays of electrodes enable precise stimulation of nerve pathways in the foot, which facilitates pain reduction and can elicit somatotopic sensations. By synchronizing the latter with pressure sensor activation, sensory perceptions below the foot can be restored.
This master thesis will focus on designing an efficient and robust calibration algorithm for MYNERVA’s neurostimulation sock. The student will collect and analyze required electrode stimulation and pressure sensor data from a group of subjects utilizing a prototype device. Various machine learning algorithms will be explored to develop an accurate and fast calibration method for both electrodes and pressure sensors. Subsequently, the resulting algorithms will undergo verification with patients to ensure effectiveness.
**Duration: 6 months (Master Thesis/Project)**
This master thesis will focus on designing an efficient and robust calibration algorithm for MYNERVA’s neurostimulation sock. The student will collect and analyze required electrode stimulation and pressure sensor data from a group of subjects utilizing a prototype device. Various machine learning algorithms will be explored to develop an accurate and fast calibration method for both electrodes and pressure sensors. Subsequently, the resulting algorithms will undergo verification with patients to ensure effectiveness.
**Duration: 6 months (Master Thesis/Project)**
Dr. Greta Preatoni
Project Leader MYNERVA
Email: greta.preatoni@wysszurich.ch
Robert John
Software Engineer MYNERVA
Email: robert.john@wysszurich.ch
Dr. Greta Preatoni Project Leader MYNERVA Email: greta.preatoni@wysszurich.ch
Robert John Software Engineer MYNERVA Email: robert.john@wysszurich.ch