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Special Issue "IMU Sensors for Human Activity Monitoring"

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 15 January 2024 | Viewed by 5363

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Special Issue Editors

Dr. Athina Tsanousa

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Centre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
Interests: activity recognition; wearable sensors; accelerometers; context-awareness; context modeling; ubiquitous computing
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Dr. Georgios Meditskos

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School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: knowledge representation; semantic web; context-based multisensor seasoning and fusion; semantic dialogue management; knowledge-driven decision making
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Dr. Stefanos Vrochidis

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Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), 57001 Thessaloniki, Greece
Interests: multimedia analysis and retrieval; multimodal analytics and fusion; semantics
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Prof. Dr. Periklis Chatzimisios

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Department of Science and Technology, International Hellenic University, 570 01 Nea Moudania, Greece
Interests: IEEE 802.11 standards; Internet of Things; low-power protocols; smart cities
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Dr. Ioannis Yiannis Kompatsiaris

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Centre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
Interests: semantic multimedia analysis; indexing and retrieval; social media and big data analysis; knowledge structures; reasoning and personalization for multimedia applications; e-health and environmental applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human activity recognition (HAR) refers to the task of understanding the activities a subject performs with the help of wearable or visual sensors. It has been a trending research topic in the last few years, and significant progress has been achieved in terms of available algorithms and relevant publications. HAR is currently employed in the majority of smart devices, such as smartphones and smartwatches, to recognize activities for fitness applications or for health applications that assist in the prevention of harmful events. Another important use of HAR is in assisted living environments, where patients can be remotely monitored by their caregivers or medical personnel.

Inertial measurement unit sensors (IMU) are widely used for human activity recognition. IMU sensors usually refer to accelerometers, gyroscopes, and magnetometers. Their efficacy in recognizing human activities is affected by their placement on the human body and the activities performed. Although accelerometers are found to have the best performance in activity recognition, their combination with other inertial sensors can prove beneficial.

This Special Issue aims to present original works on human activity recognition based on IMU sensors, with a special focus on multimodal HAR applications that include IMU sensors and their combinations with other types of sensors (e.g., physiological, visual).

Dr. Athina Tsanousa
Dr. Georgios Meditskos
Dr. Stefanos Vrochidis
Prof. Dr. Periklis Chatzimisios
Dr. Ioannis Yiannis Kompatsiaris
Guest Editors

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Keywords

human activity recognition
IMU sensors
IMU sensor fusion
multimodal fusion
wearable sensor fusion

Published Papers (8 papers)

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Research

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Open AccessArticle

Shoulder Range of Motion Measurement Using Inertial Measurement Unit–Concurrent Validity and Reliability

Jakub Kaszyński

Cezary Baka

Martyna Białecka

and

Przemysław Lubiatowski

Sensors 2023, 23(17), 7499; https://doi.org/10.3390/s23177499 - 29 Aug 2023

Viewed by 347

Abstract

This study aimed to evaluate the reliability of the RSQ Motion sensor and its validity against the Propriometer and electronic goniometer in measuring the active range of motion (ROM) of the shoulder. The study included 15 volunteers (mean age 24.73 ± 3.31) without any clinical symptoms with no history of trauma, disease, or surgery to the upper limb. Four movements were tested: flexion, abduction, external and internal rotation. Validation was assessed in the full range of active shoulder motion. Reliability was revised in full active ROM, a fixed angle of 90 degrees for flexion and abduction, and 45 degrees for internal and external rotation. Each participant was assessed three times: on the first day by both testers and on the second day only by one of the testers. Goniometer and RSQ Motion sensors showed moderate to excellent correlation for all tested movements (ICC 0.61–0.97, LOA < 23 degrees). Analysis of inter-rater reliability showed good to excellent agreement between both testers (ICC 0.74–0.97, LOA 13–35 degrees). Analysis of intra-rater reliability showed moderate to a good agreement (ICC 0.7–0.88, LOA 22–37 degrees). The shoulder internal and external rotation measurement with RSQ Motion sensors is valid and reliable. There is a high level of inter-rater and intra-rater reliability for the RSQ Motion sensors and Propriometer. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Open AccessArticle

Does Accelerometry at the Centre of Mass Accurately Predict the Gait Energy Expenditure in Patients with Hemiparesis?

and

Sensors 2023, 23(16), 7177; https://doi.org/10.3390/s23167177 - 15 Aug 2023

Viewed by 411

Abstract

Background: The aim of this study was to compare energy expenditure (EE) predicted by accelerometery (EE_Acc) with indirect calorimetry (EE_META) in individuals with hemiparesis. Methods: Twenty-four participants (12 with stroke and 12 healthy controls) performed a six-minute walk test (6MWT) during which EE_META was measured using a portable indirect calorimetry system and EE_ACC was calculated using Bouten’s equation (1993) with data from a three-axis accelerometer positioned between L3 and L4. Results: The median EE_META was 9.85 [8.18;11.89] W·kg⁻¹ in the stroke group and 5.0 [4.56;5.46] W·kg⁻¹ in the control group. The median EE_ACC was 8.57 [7.86;11.24] W·kg⁻¹ in the control group and 8.2 [7.05;9.56] W·kg⁻¹ in the stroke group. The EE_ACC and EE_META were not significantly correlated in either the control (p = 0.8) or the stroke groups (p = 0.06). The Bland–Altman method showed a mean difference of 1.77 ± 3.65 W·kg⁻¹ between the EE_ACC and EE_META in the stroke group and −2.08 ± 1.59 W·kg⁻¹ in the controls. Conclusions: The accuracy of the predicted EE, based on the accelerometer and the equations proposed by Bouten et al., was low in individuals with hemiparesis and impaired gait. This combination (sensor and Bouten’s equation) is not yet suitable for use as a stand-alone measure in clinical practice for the evaluation of hemiparetic patients. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Open AccessArticle

Biomechanical Load of Neck and Lumbar Joints in Open-Surgery Training

Ce Zhang

Charlotte Christina Roossien

Gijsbertus Jacob Verkerke

Han Houdijk

Juha M. Hijmans

and

Christian Greve

Sensors 2023, 23(15), 6974; https://doi.org/10.3390/s23156974 - 05 Aug 2023

Viewed by 468

Abstract

The prevalence of musculoskeletal symptoms (MSS) like neck and back pain is high among open-surgery surgeons. Prolonged working in the same posture and unfavourable postures are biomechanical risk factors for developing MSS. Ergonomic devices such as exoskeletons are possible solutions that can reduce muscle and joint load. To design effective exoskeletons for surgeons, one needs to quantify which neck and trunk postures are seen and how much support during actual surgery is required. Hence, this study aimed to establish the biomechanical profile of neck and trunk postures and neck and lumbar joint loads during open surgery (training). Eight surgical trainees volunteered to participate in this research. Neck and trunk segment orientations were recorded using an inertial measurement unit (IMU) system during open surgery (training). Neck and lumbar joint kinematics, joint moments and compression forces were computed using OpenSim modelling software and a musculoskeletal model. Histograms were used to illustrate the joint angle and load distribution of the neck and lumbar joints over time. During open surgery, the neck flexion angle was 71.6% of the total duration in the range of 10~40 degrees, and lumbar flexion was 68.9% of the duration in the range of 10~30 degrees. The normalized neck and lumbar flexion moments were 53.8% and 35.5% of the time in the range of 0.04~0.06 Nm/kg and 0.4~0.6 Nm/kg, respectively. Furthermore, the neck and lumbar compression forces were 32.9% and 38.2% of the time in the range of 2.0~2.5 N/kg and 15~20 N/kg, respectively. In contrast to exoskeletons used for heavy lifting tasks, exoskeletons designed for surgeons exhibit lower support torque requirements while additional degrees of freedom (DOF) are needed to accommodate combinations of neck and trunk postures. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Open AccessArticle

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks

Ismael Espinoza Jaramillo

and

Sensors 2023, 23(14), 6491; https://doi.org/10.3390/s23146491 - 18 Jul 2023

Viewed by 552

Abstract

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Open AccessArticle

Cervical Range of Motion Assessment through Inertial Technology: A Validity and Reliability Study

and

Sensors 2023, 23(13), 6013; https://doi.org/10.3390/s23136013 - 28 Jun 2023

Viewed by 511

Abstract

Inertial technology has spread widely for its comfortable use and adaptability to various motor tasks. The main objective of this study was to assess the validity of inertial measurements of the cervical spine range of motion (CROM) when compared to that of the optoelectronic system in a group of healthy individuals. A further aim of this study was to determine the optimal placement of the inertial sensor in terms of reliability of the measure, comparing measurements obtained from the same device placed at the second cervical vertebra (C2), the forehead (F) and the external occipital protuberance (EOP). Twenty healthy subjects were recruited and asked to perform flexion–extension, lateral bending, and axial rotation movements of the head. Outcome measurements of interest were CROM and mean angular velocities for each cervical movement. Results showed that inertial measurements have good reliability (0.75 < ICC < 0.9). Excellent reliability (ICC > 0.9) was found in both flexion and right lateral bending angles. All parameters extracted with EOP placement showed ICC > 0.62, while ICC < 0.5 was found in lateral bending mean angular velocities both for F and C2 placements. Therefore, the optimal sensor’s positioning emerged to be EOP. These results suggest that inertial technology could be useful and reliable for the evaluation of the CROM. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Open AccessArticle

Shoulder Range of Motion Measurement Using Inertial Measurement Unit—Validation with a Robot Arm

and

Przemysław Lubiatowski

Sensors 2023, 23(12), 5364; https://doi.org/10.3390/s23125364 - 06 Jun 2023

Cited by 2 | Viewed by 1051

Abstract

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion algorithms used to translate sensor readings into angles. The main purpose of this study was to test the accuracy of a single RSQ Motion sensor in comparison to a highly precise industrial robot. The secondary objectives were to test how the type of sensor calibration affects its accuracy and whether the time and magnitude of the tested angle have an impact on the sensor’s accuracy. We performed sensor tests for nine repetitions of nine static angles made by the robot arm in eleven series. The chosen robot movements mimicked shoulder movements in a range of motion test (flexion, abduction, and rotation). The RSQ Motion sensor appeared to be very accurate, with a root-mean-square error below 0.15°. Furthermore, we found a moderate-to-strong correlation between the sensor error and the magnitude of the measured angle but only for the sensor calibrated with the gyroscope and accelerometer readings. Although the high accuracy of the RSQ Motion sensors was demonstrated in this paper, they require further study on human subjects and comparisons to the other devices known as the gold standards in orthopedics. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Open AccessArticle

A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data

and

Sensors 2023, 23(11), 5238; https://doi.org/10.3390/s23115238 - 31 May 2023

Viewed by 850

Abstract

Bradykinesia is a cardinal hallmark of Parkinson’s disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson’s Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson’s disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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Review

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Open AccessReview

Influence of Specific Interventions on Bracing Compliance in Adolescents with Idiopathic Scoliosis—A Systematic Review of Papers Including Sensors’ Monitoring

Matteo Johann Del Furia

Sabrina Donzelli

and

Stefano Negrini

Sensors 2023, 23(17), 7660; https://doi.org/10.3390/s23177660 - 04 Sep 2023

Viewed by 628

Abstract

Adolescent idiopathic scoliosis (AIS) is a common disease that, in many cases, can be conservatively treated through bracing. High adherence to brace prescription is fundamental to gaining the maximum benefit from this treatment approach. Wearable sensors are available that objectively monitor the brace-wearing time, but their use, combined with other interventions, is poorly investigated. The aims of the current review are as follows: (i) to summarize the real compliance with bracing reported by studies using sensors; (ii) to find out the real brace wearing rate through objective electronic monitoring; (iii) to verify if interventions made to increase adherence to bracing can be effective according to the published literature. We conducted a systematic review of the literature published on Medline, EMBASE, CINAHL, Scopus, CENTRAL, and Web of Science. We identified 466 articles and included examples articles, which had a low to good methodological quality. We found that compliance a greatly varied between 21.8 and 93.9% (weighted average: 58.8%), real brace wearing time varied between 5.7 and 21 h per day (weighted average 13.3), and specific interventions seemed to improve both outcomes, with compliance increasing from 58.5 to 66% and brace wearing increasing from 11.9 to 15.1 h per day. Two comparative studies showed positive effects of stand-alone counseling and information on the sensors’ presence when added to counseling. Sensors proved to be useful tools for objectively and continuously monitoring adherence to therapy in everyday clinical practice. Specific interventions, like the use of sensors, counseling, education, and exercises, could increase compliance. However, further studies using high-quality designs should be conducted in this field. Full article

(This article belongs to the Special Issue IMU Sensors for Human Activity Monitoring)

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