Innovation portfolio

GreenSense

Smart sensors that detect changes in balance to help older adults stay safe and independent.

GreenSense enables tracking and analysis of physical movement and balance to predict and prevent falls especially for people with neurodegenerative conditions such as Parkinson’s and Dementia. The concept helps reduce risks of injuries and associated costs of treatment / recovery, that have adverse impacts on the quality of life, especially for older adults.

Status:

Pre-business

SDGs:

Good health and well-being

Industry:

Life science and medical

Impact:

Quality of life

Origin:

Research to Business

School:

School of Chemical Engineering

Established:

2025

GreenSense addresses the growing need for continuous health monitoring, especially of older vulnerable people with neurodegenerative diseases such as Parkinsons and Dementia. Today's solutions are complicated and clinic based, which misses key signals of cognitive decline over time. Such gaps in continuous assessments could miss vital indicators of physical risks arising from decline in balance that result in falls / accidents. Treatment and recovery from such falls are not only expensive for both the healthcare system and care providers, but they also tend to negatively impact the quality of life for such individuals in society.

The "GreenSense" project focuses on soft, wearable pressure and temperature sensors that monitor users' physical activities and health in real-time. These easy-to-wear sensors are designed to be integrated into a wide range of forms - insoles, patches, mats, fabrics, etc. allowing for discreet and comfortable use. The goal of the project is to enable faster interventions to prevent injuries. This also reduces the burden and costs on caregivers, helping both family members and professional care providers to optimize care through real-time health data insights.

Crisis of Falls Among Older Adults

Falls are a growing challenge
Every third person over 65 experiences a fall each year. These incidents lead to injuries, loss of confidence, and billions in healthcare costs worldwide. For those living with conditions like Parkinson’s or Dementia, the risk is even higher.

Traditional monitoring comes too late
Today, fall-risk assessments happen only a few times a year in clinical settings — providing a snapshot and not the full picture. Subtle signs of balance decline often go unnoticed until a serious fall occurs.

We need continuous, real-world insight
As care moves closer to home, physiotherapists, caregivers, and families need smarter tools to track balance and mobility in daily life — not just during clinic visits.

Why GreenSense?

GreenSense aims to combine clinical-grade accuracy with everyday usability for real-world tracking of gait and balance. Our sensors are developed to be reliable, predictive and sustainable.

Evidence-based: Close collaboration between academic and health partners.

Non-Intrusive: Designed for comfort and daily use.

Sustainable materials: Built with biodegradable sensor technology.

Scalable Analytics: Cloud platform with analytical insights and not just metrics, that are adaptable for clinics, home care, or large studies.

With GreenSense, prevention becomes proactive. People stay active, confident, and independent — and caregivers gain the insights they need to act before accidents happen.

Concept Development with flexible, biodegradable GreenSense sensors

Supporting research

Heikkinen, Mari & Başarır, Fevzihan & Miikki, Kim & Al Haj, Yazan & Mohan, Mithila & Vapaavuori, Jaana. (2024). A Low‐Cost and Do‐It‐Yourself Pressure Sensor Enable Human Motion Detection and Human–Machine Interface Applications. Advanced Sensor Research. 3. 10.1002/adsr.202300162.
Başarır, Fevzihan & Al Haj, Yazan & Zou, Fangxin & De, Swarnalok & Nguyen, An & Frey, Alexander & Haider, Ijlal & Sariola, Veikko & Vapaavuori, Jaana. (2024). Edible and Biodegradable Wearable Capacitive Pressure Sensors: A Paradigm Shift toward Sustainable Electronics with Bio‐Based Materials. Advanced Functional Materials. 34. 10.1002/adfm.202403268.
Blumen, H.M., Jayakody, O. and Verghese, J., 2023. Gait in cerebral small vessel disease, pre-dementia, and dementia: A systematic review. International Journal of Stroke, 18(1), pp.53-61.
Beauchet, O., Annweiler, C., Callisaya, M.L., De Cock, A.M., Helbostad, J.L., Kressig, R.W., Srikanth, V., Steinmetz, J.P., Blumen, H.M., Verghese, J. and Allali, G., 2016. Poor gait performance and prediction of dementia: results from a meta-analysis. Journal of the American Medical Directors Association, 17(6), pp.482-490.
Modarresi, S., Divine, A., Grahn, J.A., Overend, T.J. and Hunter, S.W., 2019. Gait parameters and characteristics associated with increased risk of falls in people with dementia: a systematic review. International psychogeriatrics, 31(9), pp.1287-1303.
Schwenk, M., Hauer, K., Zieschang, T., Englert, S., Mohler, J. and Najafi, B., 2014. Sensor-derived physical activity parameters can predict future falls in people with dementia. Gerontology, 60(6), pp.483-492.