Understanding Human Movement


Advanced inertial sensors data processing for recognizing human activities and characterizing movements.



The widespread of inertial measurement units, which can be found inside smartphones and wearables, along with size and cost reduction, opened the possibility to study human motion in a continuous on-person approach.

Continuously monitoring movements and activities of elderly and vulnerable people, enables the detection of life-threatening events such as falls. Moreover, quantification of ambulation activities allows to infer physical activity status and predict risk of falling, or other functional declines.

Indoor localization, a most desired tool for navigation or mapping people inside large and complex buildings, such as hospitals or shopping centres, can be equally achieved by continuously analysing inertial data from human gait, and applying dead-reckoning techniques combined with opportunistic sensing.

In the context of sports, or when learning new manual tasks, movement analysis can be brought outside the lab as a tool for measuring performances, giving feedback to the athlete or the worker.

In rehabilitation, motion characterization using data from inertial sensors is used to give feedback to the patient during autonomous execution of exercises, evaluating prescribed programmes of exercises and patients’ progress.



Data streams from inertial sensors are processed and analysed using machine learning techniques for automatic recognition of human motion activities and falls.

Sensor fusion techniques are used to obtain metrics which further characterize specific movements, such as joint angles, number of steps or the step length during gait.

Template matching techniques are applied for comparing the execution of certain specific movements and anomaly detection.



FhP-AICOS’ technology for falls detection with smartphones, is currently licensed to industry partners, and has showed an accuracy of 97% for simulated falls. Using a transfer learning approach, real-world falls are detected with an accuracy of 96%.

The continuous estimate of the risk of falling has shown strong correlation with a standard assessment test, the Timed Up and Go, as evaluated by an external partner.

FhP-AICOS’ solution for Indoor localization of people, with under one meter accuracy, was awarded with two top three finishes on Microsoft’s Indoor Localization Competition, has one published paper, and one granted and three pending patents.


Highlighted Projects



A solution to extend physiotherapy programs to people’s homes, based on smartphones or tablets and wearables that will be used to track the execution of the movements and give biofeedback to the user.



A new concept of home-based exergaming targeting older people threatened to develop mobility-related problems, using gait speed as a simple geriatric assessment to identify the training needs of older adults.



A web-based solution, destined to locate and characterize mobile devices users with high accuracy, both indoors and outdoors, without requiring GPS data.

Further information


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Relevant Publications


Folgado D., Barandas M., Matias R., Martins R., Carvalho M. Â., & Gamboa H. (2018). Time Alignment Measurement for Time Series. In Pattern Recognition 81, 268-279. Read here

Silva J., Sousa I., & Cardoso J. (2018). Transfer learning approach for fall detection with the FARSEEING real-world dataset and simulated falls. In 40th International Engineering in Medicine and Biology Conference (EMBC).

Pereira A., Guimarães V., & Sousa I. (2017). Joint angles tracking for rehabilitation at home using inertial sensors: a feasibility study. In PervasiveHealth '17 - Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, 146-154. Read here

Silva J., Madureira J., Tonelo C., Baltazar D., Silva C., Martins A., Alcobia C., & Sousa I. (2017). Comparing Machine Learning Approaches for Fall Risk Assessment. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), 223-230. Read here

Guimarães V., Castro L., Carneiro S., Monteiro M., Rocha T., Barandas M., Machado J., Vasconcelos M.J.M., Gamboa H., & Elias D. (2016). A motion tracking solution for indoor localization using smartphones. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Read here

Silva J., & Sousa I. (2016). Instrumented timed up and go: Fall risk assessment based on inertial wearable sensors. In 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1-6. Read here

Carneiro S., Silva J., Aguiar B., Rocha T., Sousa I., Montanha T., & Ribeiro J. (2015). Accelerometer-based methods for energy expenditure using the smartphone. In 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 151-156. Read here

Santos A., Guimarães V., Matos N., Cevada J., Ferreira C., & Sousa I. (2015). Multi-sensor exercise-based interactive games for fall prevention and rehabilitation. In PervasiveHealth '15 - Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare, 65-71. Read here

Sousa I., Silva J., & Guimarães V. (2015). Fraunhofer AICOS 360º approach on technology for falls detection, risk assessment and prevention. Read here

Aguiar B., Rocha T., Silva J., & Sousa I. (2014). Accelerometer-based fall detection for smartphones. In 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1-6. Read here

Aguiar B., Silva J., Rocha T., Carneiro S., & Sousa I. (2014). Monitoring Physical Activity and Energy Expenditure with Smartphones. In IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 664-667. Read here

Ferreira C., Guimarães V., Santos A., & Sousa I. (2014). Gamification of stroke rehabilitation exercises using a smartphone. In PervasiveHealth '14 - Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare, 282-285. Read here