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Weight-Bearing Estimation for Cane Users by Using Onboard Sensors
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
University of Malaga, Malaga, Spain.
University of Malaga, Malaga, Spain.
University of Malaga, Malaga, Spain.
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3Article in journal (Refereed) Published
Abstract [en]

Mobility is a fundamental requirement for a healthy, active lifestyle. Gait analysis is widely acknowledged as a clinically useful tool for identifying problems with mobility, as identifying abnormalities within the gait profile is essential to correct them via training, drugs, or surgical intervention. However, continuous gait analysis is difficult to achieve due to technical limitations, namely the need for specific hardware and constraints on time and test environment to acquire reliable data. Wearables may provide a solution if users carry them most of the time they are walking. We propose to add sensors to walking canes to assess user's mobility. Canes are frequently used by people who cannot completely support their own weight due to pain or balance issues. Furthermore, in absence of neurological disorders, the load on the cane is correlated with the user condition. Sensorized canes already exist, but often rely on expensive sensors and major device modifications are required. Thus, the number of potential users is severely limited. In this work, we propose an affordable module for load monitoring so that it can be widely used as a screening tool. The main advantages of our module are: (i) it can be deployed in any standard cane with minimal changes that do not affect ergonomics; (ii) it can be used every day, anywhere for long-term monitoring. We have validated our prototype with 10 different elderly volunteers that required a cane to walk, either for balance or partial weight bearing. Volunteers were asked to complete a 10 m test and, then, to move freely for an extra minute. The load peaks on the cane, corresponding to maximum support instants during the gait cycle, were measured while they moved. For validation, we calculated their gait speed using a chronometer during the 10 m test, as it is reportedly related to their condition. The correlation between speed (condition) and load results proves that our module provides meaningful information for screening. In conclusion, our module monitors support in a continuous, unsupervised, nonintrusive way during users' daily routines, plus only mechanical adjustment (cane height) is needed to change from one user to another.

Place, publisher, year, edition, pages
NLM (Medline) , 2019. Vol. 19, no 3
National Category
Other Mechanical Engineering Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-42632DOI: 10.3390/s19030509ISI: 000459941200072PubMedID: 30691145Scopus ID: 2-s2.0-85060652543OAI: oai:DiVA.org:mdh-42632DiVA, id: diva2:1287378
Available from: 2019-02-11 Created: 2019-02-11 Last updated: 2019-06-18Bibliographically approved

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