Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2024 May 25

PMID 38794059

Authors

Michele Zanoletti

Pasquale Bufano

Francesco Bossi

Francesco Di Rienzo

Carlotta Marinai

Gianluca Rho

Carlo Vallati

Nicola Carbonaro

Alberto Greco

Marco Laurino

Alessandro Tognetti

Affiliations

Soon will be listed here.

Abstract

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices' performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement.

References

Ma R, Zhao H, Wei W, Liu Y, Huang Y . Gait characteristics under single-/dual-task walking conditions in elderly patients with cerebral small vessel disease: Analysis of gait variability, gait asymmetry and bilateral coordination of gait. Gait Posture. 2021; 92:65-70. DOI: 10.1016/j.gaitpost.2021.11.007. View

Buracchio T, Dodge H, Howieson D, Wasserman D, Kaye J . The trajectory of gait speed preceding mild cognitive impairment. Arch Neurol. 2010; 67(8):980-6. PMC: 2921227. DOI: 10.1001/archneurol.2010.159. View

Studenski S . Bradypedia: is gait speed ready for clinical use?. J Nutr Health Aging. 2009; 13(10):878-80. DOI: 10.1007/s12603-009-0245-0. View

Bufano P, Laurino M, Said S, Tognetti A, Menicucci D . Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. J Med Internet Res. 2023; 25:e46778. PMC: 10753422. DOI: 10.2196/46778. View

Mannini A, Sabatini A . On-line classification of human activity and estimation of walk-run speed from acceleration data using support vector machines. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:3302-5. DOI: 10.1109/IEMBS.2011.6090896. View

Fritz S, Lusardi M . White paper: "walking speed: the sixth vital sign". J Geriatr Phys Ther. 2009; 32(2):46-9. View

Annegarn J, Spruit M, Savelberg H, Willems P, van de Bool C, Schols A . Differences in walking pattern during 6-min walk test between patients with COPD and healthy subjects. PLoS One. 2012; 7(5):e37329. PMC: 3356256. DOI: 10.1371/journal.pone.0037329. View

Onder H, Dinc E, Yucesan K, Comoglu S . The gait parameters in patients with Parkinson's Disease under STN-DBS therapy and associated clinical features. Neurol Res. 2023; 45(8):779-785. DOI: 10.1080/01616412.2023.2208478. View

Middleton A, Fritz S, Lusardi M . Walking speed: the functional vital sign. J Aging Phys Act. 2014; 23(2):314-22. PMC: 4254896. DOI: 10.1123/japa.2013-0236. View

10.

Soltani A, Aminian K, Mazza C, Cereatti A, Palmerini L, Bonci T . Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges. IEEE Trans Neural Syst Rehabil Eng. 2021; 29:1955-1964. DOI: 10.1109/TNSRE.2021.3111681. View

11.

Jehn M, Schmidt-Trucksass A, Meyer A, Schindler C, Tamm M, Stolz D . Association of daily physical activity volume and intensity with COPD severity. Respir Med. 2011; 105(12):1846-52. DOI: 10.1016/j.rmed.2011.07.003. View

12.

Storm F, Cesareo A, Reni G, Biffi E . Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review. Sensors (Basel). 2020; 20(9). PMC: 7249076. DOI: 10.3390/s20092660. View

13.

Mirelman A, Bonato P, Camicioli R, Ellis T, Giladi N, Hamilton J . Gait impairments in Parkinson's disease. Lancet Neurol. 2019; 18(7):697-708. DOI: 10.1016/S1474-4422(19)30044-4. View

14.

Schubert C, Archer G, Zelis J, Nordmeyer S, Runte K, Hennemuth A . Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease. NPJ Digit Med. 2020; 3:92. PMC: 7347580. DOI: 10.1038/s41746-020-0299-2. View

15.

Sabo A, Iaboni A, Taati B, Fasano A, Gorodetsky C . Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson's disease. Biomed Eng Online. 2023; 22(1):120. PMC: 10714555. DOI: 10.1186/s12938-023-01175-y. View

16.

Soltani A, Dejnabadi H, Savary M, Aminian K . Real-World Gait Speed Estimation Using Wrist Sensor: A Personalized Approach. IEEE J Biomed Health Inform. 2019; 24(3):658-668. DOI: 10.1109/JBHI.2019.2914940. View

17.

Karpman C, Benzo R . Gait speed as a measure of functional status in COPD patients. Int J Chron Obstruct Pulmon Dis. 2014; 9:1315-20. PMC: 4246927. DOI: 10.2147/COPD.S54481. View

18.

Waschki B, Kirsten A, Holz O, Muller K, Meyer T, Watz H . Physical activity is the strongest predictor of all-cause mortality in patients with COPD: a prospective cohort study. Chest. 2011; 140(2):331-342. DOI: 10.1378/chest.10-2521. View

19.

Avvenuti M, Carbonaro N, Cimino M, Cola G, Tognetti A, Vaglini G . Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases. Sensors (Basel). 2018; 18(11). PMC: 6263943. DOI: 10.3390/s18113811. View

20.

McGinnis R, Mahadevan N, Moon Y, Seagers K, Sheth N, Wright Jr J . A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis. PLoS One. 2017; 12(6):e0178366. PMC: 5453431. DOI: 10.1371/journal.pone.0178366. View