A Survey Investigating the Combination and Number of IMUs on the Human Body Used for Detecting Activities and Human Tracking

Reich, Olaf; Hubner, Erik; Ghita, Bogdan; Wagner, Matthias; Schäfer, Jörg

doi:10.1109/wccct49810.2020.9170009

Cited by 7 publications

(8 citation statements)

References 62 publications

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“…For the second class, the published papers include an overview of the classification performance provided by computational models (classical machine learning vs. deep learning algorithms) [28,29], and various studies focusing on proposing and improving existing computational models [27,30]. Regarding the existing review studies focused on human motion understanding, most of them mainly address the device technology used [6], the placement of the IMU sensor [31], computational algorithms [32], inertial time series feature selection [32], or body rehabilitation [33]. All the previously mentioned categories are focused on general body inspection.…”

Section: Literature Review Methodsmentioning

confidence: 99%

“…Lara et al [14] provide an overview of the features and computational methods used in motion recognition. Another study, proposed by Reich et al [6], treats the problem of the number of inertial sensors included in HAR systems. This survey provides a good overview related to the position of the inertial sensor on the body, which is a common approach in HAR systems.…”

Section: Related Work 21 Head Motion Literature Overviewmentioning

confidence: 99%

“…Regarding the age of the volunteers participating in the experiments, there was a wide variation: from 20 years [56] to 68 years old [34]. Most of the values of data acquisition frequency were as follows: 4 Hz [53], 10 Hz [28], 20 Hz [40], 48 Hz [20,36,54,71], 100 Hz [6,36,41,50,55], 125 Hz [38], 128 Hz [35], 200 Hz [47,74], 3.2 kHz [67], and 48 kHz [70]. The maximum sampling rate was found in a paper which proposed an inertial device which enabled psychotherapists to analyze mental-health-related communications [70].…”

Section: Preprocessing and Feature Extractionmentioning

confidence: 99%

“…Sensor placement [6], background clustering, and the inherent variability of how different people perform activities have been other areas of focus. Although the information from studies already conducted in this area is suggestive, a comparison between the applications of various detection methods to identify specific tasks performed by a certain part of the body with the help of inertial sensors is still missing.…”

Section: Introductionmentioning

confidence: 99%

“…To summarize the actual status of the literature, Table 1 presents 13 surveys relevant to understanding human motion based on inertial sensors. Reviewed Papers [5] 2020 Activity recognition methods Full body 8 [6] 2020 Classification of the position and number of inertial sensors Full body 58 [7] 2019…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Using Inertial Sensors to Determine Head Motion—A Review

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Dobrea

2021

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Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

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