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

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.

Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units

“…Discrepancies from previous research performances should also be read in the light of the numerosity of the experimental cohort. Instead of gathering a large number of strides from a relatively reduced sample of participants, we decided to extend the survey to a wide (n = 40) range of subjects, higher than those observed in similar studies (e.g., five subjects in [11], 14 subjects in [19], and 30 subjects in [29]). This was done to enhance the generalizability of results, despite it could have introduced interindividual variability and reduced the estimation accuracy.…”

“…In the last decade, considerable effort was devoted to provide valid and practical alternatives to overcome the limitations of traditional laboratory testing, namely, expensive equipment and time-consuming setup [ 11 , 12 ]. A clear research and development trend appeared towards systems able to capture people’s motion without expensive equipment and with limited expert knowledge required [ 13 ].…”

“…Estimating spatio-temporal parameters from IMU data is not a trivial task due to inherent sensors noise and drift problems [ 11 ]. This partly explains why these systems (despite the recent flowering of commercial products [ 11 ]) have achieved moderate and sometimes inconsistent performances to date [ 17 ], that in turn limited their widespread use for pervasive healthcare [ 19 ]. Drifts, in particular, are susceptible to produce large deviations in the calculated results when a double-integration-based sensor fusion approach is adopted [ 11 , 20 , 21 ].…”

“…This partly explains why these systems (despite the recent flowering of commercial products [ 11 ]) have achieved moderate and sometimes inconsistent performances to date [ 17 ], that in turn limited their widespread use for pervasive healthcare [ 19 ]. Drifts, in particular, are susceptible to produce large deviations in the calculated results when a double-integration-based sensor fusion approach is adopted [ 11 , 20 , 21 ]. This approach heavily depends on raw data quality even when an error state Kalman filter is applied to correct sensors’ data [ 5 , 14 ].…”

Online Measuring of Robot Positions Using Inertial Measurement Units, Sensor Fusion and Artificial Intelligence

“…IMUs are composed of sets of inertial sensors, such as gyroscopes and accelerometers [27], [28], which provide rotation speed and linear acceleration along three directions of the a body structure. There are several types of gyroscopes, and those based on MEMs [29] are the selected type for this study.…”