Friederike Popp

Advisors

Ann-Kristin Seifer (M. Sc.), Robert Richer (M. Sc.), Prof. Dr. Björn Eskofier, PD Dr. phil. Heiko Gaßner

Duration

12 / 2022 – 06 / 2023

Abstract

Standing up and sitting down are frequently performed daily tasks that require balance and muscle power [1]. The ability to perform sit-to-stand (STS) transfers is an indicator of a person ´s functional mobility and general health condition [2]. Therefore, functional performance tests have proved useful in assessing mobility impairments [3], frailty [4], and fall risk [5].

In recent years wearable technologies, such as Inertial Measurement Units (IMUs), gained popularity for clinical assessments of movement disorders. IMUs are small and light-weighted and enable continuous and unobtrusive measurements. However, the laboratory settings for clinical tests differ from the natural environment; hence these tests may not reflect the functional movements in the real world [6]. Therefore, home monitoring systems could improve the identification and treatment of mobility impairments [2].

The challenge with long-term monitoring at home is that people always must wear an extra sensor in their daily lives. Therefore, ubiquitous and non-intrusive devices that can be seamlessly integrated into the user’s everyday life are important for health monitoring applications for older adults [7]. The head or ear position could be beneficial as many people wear devices on their ears during their daily lives, e.g., headphones or hearing aids [8]. Abdollah et al. [9] proposed a head-worn accelerometer for activity recognition. The algorithm provided high accuracy for detecting posture transitions. However, it did not provide additional information on the STS transfer, such as duration, which is essential for assessing performance [10].

Adamowicz et al. [2] presented a signal-processing algorithm based on IMUs for detecting STS transfers and computing their features in daily activities. The algorithm required only one accelerometer worn on the lower back and was based on Butterworth filters and wavelet transformation. The algorithm was optimized and tested in a lab study with reference methods as ground truth as well as in an at-home study with healthy adults without a reference for the detected STS transfers. However, validation of its performance in real-world environments with a reference has not been assessed yet. Furthermore, it would be interesting to investigate the applicability and performance of the proposed algorithm by Adamowicz et al. for different sensor positions, for instance, the ear or the chest. The latter was investigated in a previously conducted study by the author of this thesis. In this study a dataset containing different sit-tostand activities as well as activities of daily life was collected. Three IMU sensors worn at the lower back, chest, and head were used. The STS detection performance for lower-back and chest sensors was compared using the optimized models provided by Adamowicz.

The first aim of this master´s thesis is to optimize the algorithm of Adamowicz et al. for the different sensor positions: hip, chest, and ear. To achieve this, a dataset from a previous project containing scripted tasks (N=15 subjects) will be used. The performance of the STS detection will be assessed for each sensor position and compared to the default parameters as provided by Adamowicz et al. Furthermore, the optimized algorithm for ear-worn sensors will be validated on unseen data for two different IMU sensor systems: a NilsPod IMU integrated into a headband and an accelerometer integrated into a hearing aid housing. For this, an existing dataset containing STS transfers from both sensors systems and 21 elderly people will be used. The last aim of this thesis is to assess frailty in older adults using STS performance. A semi-supervised study will be conducted with N= 15 older, frail adults to record STS transition using the accelerometer integrated into the hearing aid. An algorithm will be implemented and evaluated to predict the frailty status of an elderly person using the parameters derived from STS transitions.

References

[1] Atrsaei A, Dadashi F, Hansen C, Warmerdam E, Mariani B, Maetzler W, Aminian K. Postural transitions detection and characterization in healthy and patient populations using a single waist sensor. J Neuroeng Rehabil. 2020 Jun 3;17(1):70. doi: 10.1186/s12984-020-00692-4 .
[2] Adamowicz L, Karahanoglu FI, Cicalo C, Zhang H, Demanuele C, Santamaria M, Cai X, Patel S. Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back. Sensors (Basel). 2020 Nov 19;20(22):6618. doi: 10.3390/s20226618.
[3] Millor N, Lecumberri P, Gómez M, Martínez-Ramírez A, Izquierdo M. An evaluation of the 30-s chair stand test in older adults: frailty detection based on kinematic parameters from a single inertial unit. J Neuroeng Rehabil. 2013 Aug 1;10:86. doi: 10.1186/1743-0003-10-86
[4] George M. Savva, Orna A. Donoghue, Frances Horgan, Claire O’Regan, Hilary Cronin, Rose Anne Kenny, Using Timed Up-and-Go to Identify Frail Members of the Older Population, The Journals of Gerontology: Series A, Volume 68, Issue 4, April 2013, Pages 441–446, https://doi.org/10.1093/gerona/gls190
[5] A. John Campbell, Michael J. Borrie, George F. Spears, Risk Factors for Falls in a Community-Based Prospective Study of People 70 Years and Older, Journal of Gerontology, Volume 44, Issue 4, July 1989, Pages M112–M117, https://doi.org/10.1093/geronj/44.4.M112
[6] Kiani, K., Snijders, C.J., and Gelsema, E.S. ‘Computerized Analysis of Daily Life Motor Activity for Ambulatory Monitoring’. 1 Jan. 1997 : 307 – 318.
[7] Cobo A, Villalba-Mora E, Pérez-Rodríguez R, Ferre X, Rodríguez-Mañas L. Unobtrusive Sensors for the Assessment of Older Adult’s Frailty: A Scoping Review. Sensors (Basel). 2021 Apr 23;21(9):2983. doi: 10.3390/s21092983.
[8] Tobias Röddiger, Christopher Clarke, Paula Breitling, Tim Schneegans, Haibin Zhao, Hans Gellersen, and Michael Beigl. 2022. Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 3, Article 135 (September 2022), 57 pages. https://doi.org/10.1145/3550314
[9] Abdollah V, Dief TN, Ralston J, Ho C, Rouhani H. Investigating the validity of a single tri-axial accelerometer mounted on the head for monitoring the activities of daily living and the timed-up and go test. Gait Posture. 2021 Oct;90:137-140. doi: 10.1016/j.gaitpost.2021.08.020.
[10] Ganea R, Paraschiv-Ionescu A, Büla C, Rochat S, Aminian K. Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people. Med Eng Phys. 2011 Nov;33(9):1086-93. doi: 10.1016/j.medengphy.2011.04.015