Currently there are a lot of challenges regarding the assessment of Parkinson’s disease symptoms. To solve these challenges, we have designed and developed a shirt with electronic sensors accompanied with software for data processing and analysis.


There are 10 million people living with Parkinson’s disease (PD) today and the prevalence of the disease is growing as the population ages [1].

Clinicians currently monitor disease progression by periodically assessing severity of symptoms using the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) [2]. This assessment is prone to a variety of challenges. Firstly, the subjective assessments of individual doctors allow for varying severity classifications. Secondly, the assessment depends strongly on the momentary state of the patient, as the symptoms of PD can vary over time [3]. Thirdly, the 5 severity classifications (0 to 4) of the MDS-UPDRS further limit the precision of assessment. An objective quantification of PD symptoms thus aids in minimizing variability and raising precision in providing reliable assessment of severity.

We have chosen to focus on the 4 cardinal symptoms of PD; tremor, bradykinesia, muscle rigidity and postural instability. Tremor is the first sign of PD in 70% of cases and over the course of the disease 75% of patients develop tremors. Additionally, as the disease progresses the PD patients will develop bradykinesia, muscle rigidity and postural instability, hence the focus [4].


Our solution is a shirt with movement and muscle activity sensors implemented, allowing for collection of precise movement and muscle activity data. An accompanied software solution processes and analyzes the data to conform with the MDS-UPDRS reliably. The raw and processed data will then be encrypted and safely stored on a cloud as well as a memory device.

Tremor, bradykinesia and postural instability are movement related symptoms and will be measured and quantified using movement sensors. Muscle rigidity will be measured and quantified using a combination of movement and muscle activity sensors. Several scientific articles support the use of movement and muscle activity sensors as a reliable method of quantifying PD symptoms [5].

The reason that we implemented the aforementioned technologies in something as discrete as a shirt is to minimize inconvenience for patients as well as to avoid stigmatization in everyday life. A shirt also allows for measurements of symptoms in the entire upper body, e.g. muscle rigidity can be present in many different areas of the body. Furthermore, most PD patients are cognitively impaired [6], therefore our solution will be fully automated and gather data passively, thus requiring no patient interaction.

Our solution is a shirt, but we acknowledge that our technology could be implemented in other textile wearables as well.


4.1 Unique value propositions

  • Assessing all 4 cardinal symptoms using only one product.
  • Discrete solution that passively collects data.
  • Personalized treatment using machine learning.
  • Can be used in a clinical setting and daily life.

4.2 Customers

Our target customers are pharmaceutical companies who can use our product to quantify treatment effectiveness and thereby having a stronger basis for evaluation in drug development and medical institutions as the product can be used to significantly reduce resources spend on dosage adjustment of medication.

4.3 Key partners and resources

In the initial stages of product development, we have a need for patient data and feedback to improve our complete solution. In order to accomplish this, we want to partner up for pharmaceutical companies, e.g. Lundbeck, and medical institutions, e.g. hospitals. As well as this, we would partner up with patient organizations, e.g. Parkinsonforeningen to establish a relationship with the end-user, i.e. PD patients.

4.4 Cost structure

One of the main costs will be materials and manufacturing of the product itself as well as the product and software development. Furthermore, patents and medical graded certificates, e.g. CE marking.

4.5 Product cost estimation

Our estimated price per unit is approximately 429 DKK with a minimum order quantity of 1000 units.

4.6 Revenue streams

Our company sales strategy is based on business-to-business, both having a one-time payment for the physical product and a subscription fee for software and product maintenance.


Our solution provides the possibility for objective assessments of motor symptoms. The data collected by our product can contribute to research and development of new treatment modalities. Furthermore, having data from each individual patient also allows for a much more personalized treatment, as the patient’s response to medication can be assessed both more objectively and more in depth, ultimately improving the patients quality of life [7]. Additionally, it lowers the number of clinical visits, thus reducing inconvenience for patients, as well as decreasing the societal cost of such visits.


We have gathered a versatile team, with a plethora of varying skills and experiences. Each team member is essential to the development of our final product.

Name Motivation Study E-mail
Mohammad Filfil Mohammad Filfil – Student of electrical engineering at the Technical university of Denmark. Mohammad has his own consulting company with 6 years of experience with establishing successful tech startups such as Ernit, IPM, Haxi, and Brixx. Student of electrical engineering – DTU
Wahib Abboud Wahib has just finished his Master’s degree specializing in polymers. He finished his masters and has been working with Oticon A/S and Liita Care, working on medical devices. Graduate of Materials and manufacturing engineering – DTU
Marcus Krogh Nielsen In his studies Marcus focusses on machine learning and control theory, as well as teaching classes in constrained optimization at DTU. Student of mathematical modelling – DTU
Bilal Benomar Bilal teaches anatomy and physiology at the University of Copenhagen, and has a broad knowledge of the biomedical sciences. Bilal also works as a research assistant at Neurocentret, Rigshospitalet. Medical student – KU
Arvan Vali Maani Arvan has experience in fluid dynamics and works as a design engineer. Student of mechanical engineering – DTU
Bayram Bayram Bayram is the connecting element between the biomedical and technical areas, specializing in signal processing. Bayram is working as a IT specialist at Rigshospitalet. Student of biomedical engineering – DTU
Osama Ibrahim Osama has experience in sales and teaching as well as a drive and interest in the newest medical technology. Dentistry student – KU









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