Ortega Bastidas P1, Gomez Arias B2, Aqueveque Navarro P2, Saavedra Rodriguez F2, Cano de la Cuerda R3
1Universidad de Concepcion, Kinesiology, Concepcion, Chile, 2Universidad de Concepcion, Electrical Engineering, Concepcion, Chile, 3Universidad Rey Juan Carlos, Physiotherapy, Occupational Therapy, Rehabilitation and Physical Medicine, Madrid, Spain
Background: Traditionally the clinical assessment of patients was performed through observational measures, lending themselves to possible biases. The existing methods and technologies are numerous. However, these systems are expensive, not being available to all users and professionals for training, time and experience in the interpretation of results. In Chile there are many rural locations that do not have the resources to access advanced technological assessments, so it is important to create, design and implement low-cost devices that allow users and professionals from these locations to get closer to objective and quality human assessment opportunities. In this context, the use of mobile applications could be an effective and efficient option to incorporate objective technological tools into clinical practice in rehabilitation and movement analysis.
Purpose: To study the construct validity a mobile application to assess the risk of falls using Timed Up and Go (TU&G) test using inertial sensors in healthy subjects.
Methods: We evaluated 20 healthy subjects, 10 men and 10 women, in the Biomedical Engineering Laboratory and in the Department of Kinesiology at University of Concepcion. All participants agreed to participate voluntarily and none had neurological, musculoskeletal, orthopedic or systematic pathology that interfered with their ability to walk. In a first evaluation, measurements of the risk of falls were made applying the conventional TU&G and simultaneously an IMU sensor was used in the lumbar area, developed by the research team, for the study of construct validity. To analyze the data with inertial sensors, the TU & G was divided into 6 phases:
1. Getting up,
2. Walking towards the brand,
3. Turning on the mark,
4. Walking towards the chair,
5. Turning when sitting down and
6. Sitting down.
Three measurements were made for each subject obtaining the average time in seconds that lasted each phases, all the phases were recorded with a videocamera.
Results: Preliminarily, 20 people were measured with the mobile app. The maximum error per measurement system was 14 [ms], 10 [ms] of the sensor sampling frequency (100 [Hz]) and 4 [ms] of the video camera (240 [fps]). The total time of the test was measured satisfactorily with an average difference of 11 [ms] with respect the video recorded and the algorithm of the mobile app identifies automatically each of the previously described phases, whose maximum average difference was 150 [ms] with the video recorded, occurring in the phase where the first turn was executed.
Conclusion(s): This app linked to inertial sensors would be the first designed in Chile in our knowledge to offer objective and immediate information on the risk of falls, based on the TU&G.The results obtained are promising, demonstrating that the methodology used could be replicable in patients with various pathologies.
Implications: This would make it possible to provide low-cost technology, facilitating data collection in less time and allowing assessments in rural areas and difficult access, giving rehabilitation centers the possibility of remote monitoring of their patients´ movement analysis and the risk of falls. Future studies should assess their psychometric properties and implementation in population with specific neurological pathologies.
Keywords: Mobile applications, Risk of fall, Movement Analysis Systems
Funding acknowledgements: Support of postdoctoral project 3180551 of the Chilean Fund of Science and Technology development. (FONCECYT).
Purpose: To study the construct validity a mobile application to assess the risk of falls using Timed Up and Go (TU&G) test using inertial sensors in healthy subjects.
Methods: We evaluated 20 healthy subjects, 10 men and 10 women, in the Biomedical Engineering Laboratory and in the Department of Kinesiology at University of Concepcion. All participants agreed to participate voluntarily and none had neurological, musculoskeletal, orthopedic or systematic pathology that interfered with their ability to walk. In a first evaluation, measurements of the risk of falls were made applying the conventional TU&G and simultaneously an IMU sensor was used in the lumbar area, developed by the research team, for the study of construct validity. To analyze the data with inertial sensors, the TU & G was divided into 6 phases:
1. Getting up,
2. Walking towards the brand,
3. Turning on the mark,
4. Walking towards the chair,
5. Turning when sitting down and
6. Sitting down.
Three measurements were made for each subject obtaining the average time in seconds that lasted each phases, all the phases were recorded with a videocamera.
Results: Preliminarily, 20 people were measured with the mobile app. The maximum error per measurement system was 14 [ms], 10 [ms] of the sensor sampling frequency (100 [Hz]) and 4 [ms] of the video camera (240 [fps]). The total time of the test was measured satisfactorily with an average difference of 11 [ms] with respect the video recorded and the algorithm of the mobile app identifies automatically each of the previously described phases, whose maximum average difference was 150 [ms] with the video recorded, occurring in the phase where the first turn was executed.
Conclusion(s): This app linked to inertial sensors would be the first designed in Chile in our knowledge to offer objective and immediate information on the risk of falls, based on the TU&G.The results obtained are promising, demonstrating that the methodology used could be replicable in patients with various pathologies.
Implications: This would make it possible to provide low-cost technology, facilitating data collection in less time and allowing assessments in rural areas and difficult access, giving rehabilitation centers the possibility of remote monitoring of their patients´ movement analysis and the risk of falls. Future studies should assess their psychometric properties and implementation in population with specific neurological pathologies.
Keywords: Mobile applications, Risk of fall, Movement Analysis Systems
Funding acknowledgements: Support of postdoctoral project 3180551 of the Chilean Fund of Science and Technology development. (FONCECYT).
Topic: Human movement analysis; Disability & rehabilitation; Robotics & technology
Ethics approval required: Yes
Institution: UNIVERSIDAD DE CONCEPCION
Ethics committee: Biosecurity, Bioethical and Ethical Committee
Ethics number: 3180551
All authors, affiliations and abstracts have been published as submitted.
