Jovana Zlatanovic

Advisors
Arne Küderle (M.Sc.), Prof. Dr. Björn Eskofier

Duration
03/2019 – 09/2019

Abstract
Gait analysis is regularly used in a clinical environment to monitor the progression of neurodegenerative diseases, for example Parkinson’s disease. Parkinson’s patients suffer from declined mobility which also effects their gait patterns. Through gait tests it is possible to detect these changes in the gait parameters. This knowledge helps to understand the progression of the disease and therefore, can improve medical therapy and treatment [1, 6].

Over the past years, various gait measurement systems were proposed in literature. A promising approach is the use of inertial measurement units (IMU). These sensors are able to measure the 3D acceleration and rate of rotation by means of an accelerometer and gyroscope, respectively [4]. When such sensors are attached to the lower extremities of a patient it is possible to calculate spatial temporal gait parameters [2, 3, 4]. Such wearable gait analysis systems are in the process of getting established in a clinical context, therefore recent research focuses on the possibility of home monitoring. This would allow to gather parameters continuously over a long time period from purposeful walking. The acquired data can be used to improve healthcare services and treatment for patients [5, 6].

With home monitoring in mind it is important to ensure that these potential sensor systems are easy to use and able to operate unobtrusively. Therefore, it is critical to choose an appropriate sensor location and attachment method. While many existing clinical systems choose the foot as their primary sensor location, the exact position and mean of attachment vary [3, 7, 8]. Therefore, it is important to assess if changing the sensor position will affect the quality of the calculated gait parameters. Previous research suggests that this is the case [3, 9]. However, no clear system independent recommendation for IMU positioning exists.

The goal of this work is to methodically investigate the influence of different sensor positions on the raw IMU-signal as well as the calculated spatial-temporal gait parameters. The basis for this enquiry will be data acquired from simultaneously recording sensors at different positions at the shoe. The comparison of three or four sensor positions are planned. As a second aspect, the sturdiness of the sensor attachment will be investigated. The fixation of the sensor will be loosened for a subset of measurements to investigate its impact on the data quality. Finally, by combining both investigated aspects, this work aims to provide a recommendation for an ideal sensor attachment.

References:

1. BARTH, Jens, et al. Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson’s disease. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. S. 868-871.
2. ATALLAH, Louis, et al. “Sensor positioning for activity recognition using wearable accelerometers.” IEEE transactions on biomedical circuits and systems 5.4 (2011): 320-329.
3. ANWARY, Arif Reza; YU, Hongnian; VASSALLO, Michael. Optimal foot location for placing wearable IMU sensors and automatic feature extraction for gait analysis. IEEE Sensors Journal, 2018, 18. Jg., Nr. 6, S. 2555-2567.
4. ZIMMERMANN, Tobias; TAETZ, Bertram; BLESER, Gabriele. IMU-to-segment assignment and orientation alignment for the lower body using deep learning. Sensors, 2018, 18. Jg., Nr. 1, S. 302.
5. GHOBADI, Mostafa; ESFAHANI, Ehsan T. A robust automatic gait monitoring approach using a single IMU for home-based applications. Journal of Mechanics in Medicine and Biology, 2017, 17. Jg., Nr. 05, S. 1750077.
6. MAZILU, Sinziana, et al. A wearable assistant for gait training for Parkinson’s disease with freezing of gait in out-of-the-lab environments. ACM Transactions on Interactive Intelligent Systems (TiiS), 2015, 5. Jg., Nr. 1, S. 5.
7. FALBRIARD, Mathieu, et al. Accurate estimation of running temporal parameters using foot-worn inertial sensors. Frontiers in physiology, 2018, 9. Jg., S. 610.
8. KLUCKEN, Jochen, et al. Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease. PloS one, 2013, 8. Jg., Nr. 2, S. e56956.
9. PERUZZI, Ai; DELLA CROCE, Ugo; CEREATTI, Andrea. Estimation of stride length in level walking using an inertial measurement unit attached to the foot: A validation of the zero velocity assumption during stance. Journal of Biomechanics, 2011, 44. Jg., Nr. 10, S. 1991-1994.