FallSensing: fall risk assessment and exercises for fall prevention

We have more news about FallSensing. This project that aims to develop tools for fall risk assessment, falls monitoring and prevention had many advances through the past months.

Researchers and designers together created new games and couldn’t wait to test them! With the help of the volunteers in the user network COLABORAR, for sure. Therefore, we went to a living centre in Porto and organized some research activities. We started with the recruitment of participants, and then we applied tests and questionnaires to assess the fall risk of each user. Then, we scheduled tests with users at the centre. Both fall risk assessment and exercises for prevention were accompanied by a physiterapist. Researchers took notes of some details related to usability issues while seniors were playing the games. Researchers were also paying attention to questions related to comfort of body sensors that older adults were using to capture movements during the game, enabling the evaluation of their performance.

The feedback from users was great, as they said they enjoyed playing in group with their friends. The laughts also indicate that they had a good time playing the FallSensing games. The team could not be as happy with the success of FallSensing!

Fraunhofer is testing sensors for eating and drinking activities detection

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Fraunhofer Portugal AICOS’ researchers developed a new tool that enables the automatic detection and recognition of eating and drinking activities. The aim of this research, within the project DEMSmartMoves, is recognizing the activity pattern of each user regarding meal intake and then detect these activities. In the case the user is missing out on a meal, the system should be able to automatically send reminders to its user. A smartphone and a wearable device to be used in the wrist comprise the system, which might be very useful for older adults, who frequently miss out meals, due to forgiveness, lack of appetite or being unable to cook. This situation usually results in nutrition deficits in the old age.

After an initial set of tests with users  at a day care centre, researchers are testing again the devices with older adults. Volunteers from the user network COLABORAR took this challenge, using the sensor in the wrist for 5 hours; the test began in the morning and finished in the afternoon. Between this period, participants performed their daily activities at the centre, such as playing board games, talking, walking around and also eating lunch meal and drinking.

COLABORAR volunteers enjoyed being part of this investigation and hope that in the future, this technology will help other seniors.

SmartReminders – Tests with sensors

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COLABORAR provided users to test the sensors used to detect the movements of the arm. This sensors are being used in the scope of the thesis SmartReminders, under development at Fraunhofer AICOS, with the purpose of monitoring daily activities such as eating or drinking.

The first tests were conducted with a sample of 20 seniors at a day-care centre. Participants had the sensors in their wrists while taking their meals.

Thank you all participants! You always receive us warmly at the centre!

Tests for FallSensing


Fraunhofer Portugal AICOS has been developing interactive games for fall prevention (project FallSensing) and we have just made the first tests with older adults at a Day Care Centre in Porto.

We took equipment to the centre and introduced the game. Then, we let participants try out the game and have fun! Researchers and the designer working on this project performed some observations and were happy to see how seniors organized to participate and how they interacted with each other, building teams and socialize around the new game.

We will be back soon to the centre to test new games!

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Active@Home: first tests with users at the Living Lab


COLABORAR organized a session of tests for the project Active@Home.

Within this project, television games for physical exercise and fall prevention are being designed and developed. Users must use some sensors to detect physical movements, as well. In this first test session  with real end users, researchers tested the acceptance of the demo and collected some datasets using the sensors.

Participants seemed to enjoy and we are grateful for the precious help!

World Diabetes Day

November 14th is World Diabetes Day. The day was created by the International Diabetes Federation to help increase Diabetes awareness. Diabetes is a disease that affects people worldwide and its complications are blindness, heart attack, kidney failure and limb amputation. Blindness and amputation are consequences of Diabetic Rethinopathy and Diabetic Neuropathy, respectively. Researchers from  Fraunhofer Portugal AICOS are developing new solutions to improve the early detection of these Diabetes complications. COLABORAR wants to mark the date by highlighting those projects:

  • EyeFundusScope – “Mobile-based risk assessment of Diabetic Retinopathy by image processing” comprises an ophthalmoscopic adapter attached to a smartphone with an application that processes the images of the eye fundus captured by the optical adapter. The app allows the detection of micro-aneurysms, which are the first visible signs of Diabetic Rethinopathy, on an non-expert monitoring context, enabling an early pre-diagnosis;
  • NeuropathyDetect – “Detection of Peripheral Neuropathy in Diabetes patients” consists of using plantar pressure sensors and smartphone built-in accelerometers to collect gait data that is then analyzed by a mobile application. It enables to identify early signs of Diabetic Peripheral Neuropathy in diabetic patients who therefore must adopt protective measures to prevent feet injuries and be followed-up by their medical doctor, in order to improve their treatment.

You can see a news piece by SIC Notícias on these two Fraunhofer Portugal AICOS’s projects below (in Portuguese).

SmartRecovery: tests with patients


The Master Thesis “Gait analysis in patients recovering from total joint replacement using body fixed sensors” (SmartRecovery) aims to study the gait of patients after a Total Knee Replacement (TKR), by measuring body movements, body mechanics and the activity of the muscles.

Several tests were conducted with the experimental group, comprised by patients recovering from TKR, and the control group, comprised by senior volunteers of COLABORAR. The tests took place respectively at the hospital and at Fraunhofer. The tests with volunteers will enable to differentiate the gait of patients who underwent a Total Knee Replacement and highlight the changes of gait between them and the people who did not undergo surgery for TKR.

NeuropathyDetect: tests for early detection of Neuropathy in people with Diabetes


Some patients affected by Diabetes could develop Peripherical Neuropathy. At Fraunhofer Portugal, in the scope of the Master Thesis NeuropathyDetect, researchers are studying the gait of people with Diabetes. COLABORAR recruited volunteers for the tests, consisting in gait tests using sensors. Researchers could analyse the walking patterns of people with and without diabetes.

Thank you to all the participants, both at the centers and Fraunhofer’s facilities, for participating in the tests, allowing researchers to take an important step towards the identification of early signs of peripheral neuropathy in people with Diabetes.


Kneegraphy: tests with volunteers


In the scope of the Master Thesis “Classification of knee arthropathy with accelerometer-based vibroarthrography” (KneeGraphy), researchers are studying the knees of people with knee osteo-arthropathy, which is a condition that affects many seniors. In order to do so, researchers are developing an accelerometer-based system for knee data acquisition and data feature extraction for the differentiation between a pathological and a non-pathological knee. Several tests were conducted with seniors diagnosed with knee osteo-arthropathy and other volunteers for the control group.

Tests for the thesis mActivityClassify


A prototype has been developed in the scope of the master thesis mActivityClassify. This is a solution that allows to classify activities of daily living. It consists of a real time classification algorithm using an Android smartphone in the pocket and an external smartwatch in the forearm. We asked participants to try out the system, by making some everyday gestures we use in activities of daily living.

The correct assessment of different activities can  be directly applied in the gamification of rehabilitation exercises for patients in the process of post-stroke rehabilitation.