How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

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Abstract

Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.

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Most cited references 41

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Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes.

K. Aminian, B Najafi, C Büla … (2002)

In this study we describe an ambulatory system for estimation of spatio-temporal parameters during long periods of walking. This original method based on wavelet analysis is proposed to compute the values of temporal gait parameters from the angular velocity of lower limbs. Based on a mechanical model, the medio-lateral rotation of the lower limbs during stance and swing, the stride length and velocity are estimated by integration of the angular velocity. Measurement's accuracy was assessed using as a criterion standard the information provided by foot pressure sensors. To assess the accuracy of the method on a broad range of performance for each gait parameter, we gathered data from young and elderly subjects. No significant error was observed for toe-off detection, while a slight systematic delay (10 ms on average) existed between heelstrike obtained from gyroscopes and footswitch. There was no significant difference between actual spatial parameters (stride length and velocity) and their estimated values. Errors for velocity and stride length estimations were 0.06 m/s and 0.07 m, respectively. This system is light, portable, inexpensive and does not provoke any discomfort to subjects. It can be carried for long periods of time, thus providing new longitudinal information such as stride-to-stride variability of gait. Several clinical applications can be proposed such as outcome evaluation after total knee or hip replacement, external prosthesis adjustment for amputees, monitoring of rehabilitation progress, gait analysis in neurological diseases, and fall risk estimation in elderly.

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Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

André Henriksen, Martin Haugen Mikalsen, Ashenafi Woldaregay … (2018)

Background New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. Objective The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. Methods We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. Results We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. Conclusions The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.

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Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring.

Françios J G Vingerhoets, Arash Salarian, Catherine Dehollain … (2004)

An ambulatory gait analysis method using body-attached gyroscopes to estimate spatio-temporal parameters of gait has been proposed and validated against a reference system for normal and pathologic gait. Later, ten Parkinson's disease (PD) patients with subthalamic nucleus deep brain stimulation (STN-DBS) implantation participated in gait measurements using our device. They walked one to three times on a 20-m walkway. Patients did the test twice: once STN-DBS was ON and once 180 min after turning it OFF. A group of ten age-matched normal subjects were also measured as controls. For each gait cycle, spatio-temporal parameters such as stride length (SL), stride velocity (SV), stance (ST), double support (DS), and gait cycle time (GC) were calculated. We found that PD patients had significantly different gait parameters comparing to controls. They had 52% less SV, 60% less SL, and 40% longer GC. Also they had significantly longer ST and DS (11% and 59% more, respectively) than controls. STN-DBS significantly improved gait parameters. During the stim ON period, PD patients had 31% faster SV, 26% longer SL, 6% shorter ST, and 26% shorter DS. GC, however, was not significantly different. Some of the gait parameters had high correlation with Unified Parkinson's Disease Rating Scale (UPDRS) subscores including SL with a significant correlation (r = -0.90) with UPDRS gait subscore. We concluded that our method provides a simple yet effective way of ambulatory gait analysis in PD patients with results confirming those obtained from much more complex and expensive methods used in gait labs.

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Journal

Journal ID (nlm-ta): Sensors (Basel)

Journal ID (iso-abbrev): Sensors (Basel)

Journal ID (publisher-id): sensors

Title: Sensors (Basel, Switzerland)

Publisher: MDPI

ISSN (Electronic): 1424-8220

Publication date (Electronic): 22 July 2020

Publication date Collection: August 2020

Volume: 20

Issue: 15

Electronic Location Identifier: 4090

Affiliations

[1 ]Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany; eric.fischer@ 123456student.hpi.uni-potsdam.de

[2 ]NETLAB, Department of Computer Engineering, Bogazici University, 34342 Istanbul, Turkey; can.tunca@ 123456boun.edu.tr (C.T.); ersoy@ 123456boun.edu.tr (C.E.)

[3 ]Division of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany; mbrahms@ 123456uni-potsdam.de (C.M.B.); urs.granacher@ 123456uni-potsdam.de (U.G.)

Author notes

[* ]Correspondence: lin.zhou@ 123456hpi.de (L.Z.); bert.arnrich@ 123456hpi.de (B.A.)

Author information

Lin Zhou https://orcid.org/0000-0001-9916-3878

Can Tunca https://orcid.org/0000-0002-7532-8428

Cem Ersoy https://orcid.org/0000-0001-7632-7067

Urs Granacher https://orcid.org/0000-0002-7095-813X

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Publisher ID: sensors-20-04090

DOI: 10.3390/s20154090

PMC ID: 7435687

PubMed ID: 32707987

SO-VID: f97026c0-5472-467b-bb2d-f1d99930bfa4

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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

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Most cited references 41

Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes.

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring.

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