Wearable sensing of physical activity


We help our customers to acquire and analyse data from wearable movement sensors, for example, to automatically detect the amount of daily physical activity and the types of physical activities.

With the VTT algorithms, we can analyse raw, real-world movement sensor data, for instance from an accelerometer, in order to detect several types of physical activities such as lying down, sitting, standing, walking, running, bicycling, rowing, playing football, sleeping, etc. We are offering the existing algorithms, software applications for data annotation and visualization, and know-how in wearable sensing, signal analysis, algorithm development, validation studies and software development.

Unlike many traditional classifiers, the VTT algorithms have a low computational cost, and are thus also suitable for embedded use, for example in mobile phones and other wearable devices. Our algorithms and software have been developed in several scientific studies. Our publications have been widely cited and are among the top-publications in the world.

In addition to activity types, more detailed analysis of, for example the steps or arm movements, is possible, as well as an estimation of total energy expenditure, based on data from unobtrusive, wearable sensors. We have, in addition to data analysis, worked on the selection of sensors for validation studies, the design of a data collection protocol as well as with the data collection. We are also developing software applications for data annotation and visualization.

The algorithms have been developed using the carefully collected and well-annotated data libraries from several research studies. We have developed algorithms for customers in several projects. Our customers include Firstbeat Technologies and Nokia.

Where can we apply motion analysis?

The algorithms can be used in different application domains, for instance  in self-monitoring, preventive care, rehabilitation programs, occupational health, stress studies, exposome studies, etc. The inferred information can be used to profile users, to coach the users, and so on.

In our projects, we can utilize the existing know-how, algorithms, software applications for data annotation and visualization, and the facilities of a Personal Health Systems laboratory with, for example, treadmill, exercise bike, rowing machine and devices for reference measurements.

The work on monitoring physical activities was started in a national research project named Palantir “Context sensing with wearable sensors and data fusion”. Currently this work is applied in the European project called PredictND for studying possibilities of using movement data in prediction of dementia www.predictnd.eu.


 Related publications

 “Activity classification using realistic data from wearable sensors”; Pärkkä, Juha; Ermes, Miikka; Korpipää, Panu; Mäntyjärvi, Jani; Peltola, Johannes; Korhonen, Ilkka. IEEE Transactions on Information Technology in Biomedicine. Vol. 10 (2006) No: 1, 119–128


“Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions”; Ermes, Miikka; Pärkkä, Juha; Mäntyjärvi, Jani; Korhonen, Ilkka. IEEE Transactions on Information Technology in Biomedicine. Vol. 12 (2008) No: 1, 20–26


“Estimating intensity of physical activity: a comparison of wearable accelerometer and gyro sensors and 3 sensor locations”; Pärkkä, Juha; Ermes, Miikka; Antila, Kari; Gils, Mark van; Mänttäri, Ari; Nieminen, Heikki. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon, France, 23–26 August 2007. IEEE Engineering in Medicine and Biology Society (2007) , 386

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