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11 pages, 2457 KiB

Open AccessArticle

Accuracy Validation of a Sensor-Based Inertial Measurement Unit and Motion Capture System for Assessment of Lower Limb Muscle Strength in Older Adults—A Novel and Convenient Measurement Approach

by Ye Zhu, Haojie Li, Xie Wu and Nan Chen

Sensors 2024, 24(18), 6040; https://doi.org/10.3390/s24186040 - 18 Sep 2024

Viewed by 1133

Abstract

(1) Background: The aim of this study was to assess lower limb muscle strength in older adults during the transfer from sitting to standing (STS) using an inertial measurement unit (IMU). Muscle weakness in this population can severely impact function and independence in [...] Read more.

(1) Background: The aim of this study was to assess lower limb muscle strength in older adults during the transfer from sitting to standing (STS) using an inertial measurement unit (IMU). Muscle weakness in this population can severely impact function and independence in daily living and increase the risk of falls. By using an IMU, we quantified lower limb joint moments in the STS test to support health management and individualized rehabilitation program development for older adults. (2) Methods: This study involved 28 healthy older adults (13 males and 15 females) aged 60–70 years. The lower limb joint angles and moments estimated using the IMU were compared with a motion capture system (Mocap) (pair t-test, ICC, Spearman correlations, Bland–Altman plots) to verify the accuracy of the IMU in estimating lower limb muscle strength in the elderly. (3) Results: There was no significant difference in the lower limb joint angles and moments calculated by the two systems. Joint angles and moments were not significantly different (p > 0.05), and the accuracy and consistency of the IMU system was comparable to that of the Mocap system. For the hip, knee, and ankle joints, the ICCs for joint angles were 0.990, 0.989, and 0.885, and the ICCs for joint moments were 0.94, 0.92, and 0.89, respectively. In addition, the results of the two systems were highly correlated with each other: the r-values for hip, knee, and ankle joint angles were 0.99, 0.99, and 0.96, and the r-values for joint moments were 0.92, 0.96, and 0.85. In the present study, there was no significant difference (p > 0.05) between the IMU system and the Mocap system in calculating lower limb joint angles and moments. (4) Conclusions: This study confirms the accuracy of the IMU in assessing lower limb muscle strength in the elderly. It provides a portable and accurate alternative for the assessment of lower limb muscle strength in the elderly. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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10 pages, 1779 KiB

Open AccessArticle

Smartphone Assessment of the Sitting Heel-Rise Test

by Gustavo O. Hoffmann, Edilson Borba, Eduardo H. Casarotto, Gisele Francine Devetak, Ramzi Jaber, John G. Buckley and André L. F. Rodacki

Sensors 2024, 24(18), 6036; https://doi.org/10.3390/s24186036 - 18 Sep 2024

Viewed by 688

Abstract

The study presents a new approach for assessing plantarflexor muscles’ function using a smartphone. The test involves performing repeated heel raises for 60 s while seated. The seated heel-rise test offers a simple method for assessing plantarflexor muscles’ function in those with severe [...] Read more.

The study presents a new approach for assessing plantarflexor muscles’ function using a smartphone. The test involves performing repeated heel raises for 60 s while seated. The seated heel-rise test offers a simple method for assessing plantarflexor muscles’ function in those with severe balance impairment who are unable to complete tests performed while standing. The study aimed to showcase how gyroscopic data from a smartphone placed on the lower limb can be used to assess the test. Eight participants performed the seated heel-rise test with each limb. Gyroscope and 2D video analysis data (60 Hz) of limb motion were used to determine the number of cycles, the average rise (T-rise), lowering (T-lower), and cycle (T-total) times. The number of cycles detected matched exactly when the gyroscope and kinematic data were compared. There was good time domain agreement between gyroscopic and video data (T-rise = 0.0005 s, T-lower = 0.0013 s, and T-total = 0.0017 s). The 95% CI limits of agreement were small (T-total −0.1118, 0.1127 s, T-lower −0.1152, 0.1179 s, and T-total −0.0763, 0.0797 s). Results indicate that a smartphone placed on the thigh can successfully assess the seated heel-rise test. The seated heel-rise test offers an attractive alternative to test plantarflexor muscles’ functionality in those unable to perform tests in standing positions. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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34 pages, 2564 KiB

Open AccessArticle

Achieving More with Less: A Lightweight Deep Learning Solution for Advanced Human Activity Recognition (HAR)

by Sarab AlMuhaideb, Lama AlAbdulkarim, Deemah Mohammed AlShahrani, Hessah AlDhubaib and Dalal Emad AlSadoun

Sensors 2024, 24(16), 5436; https://doi.org/10.3390/s24165436 - 22 Aug 2024

Viewed by 1376

Abstract

Human activity recognition (HAR) is a crucial task in various applications, including healthcare, fitness, and the military. Deep learning models have revolutionized HAR, however, their computational complexity, particularly those involving BiLSTMs, poses significant challenges for deployment on resource-constrained devices like smartphones. While BiLSTMs [...] Read more.

Human activity recognition (HAR) is a crucial task in various applications, including healthcare, fitness, and the military. Deep learning models have revolutionized HAR, however, their computational complexity, particularly those involving BiLSTMs, poses significant challenges for deployment on resource-constrained devices like smartphones. While BiLSTMs effectively capture long-term dependencies by processing inputs bidirectionally, their high parameter count and computational demands hinder practical applications in real-time HAR. This study investigates the approximation of the computationally intensive BiLSTM component in a HAR model by using a combination of alternative model components and data flipping augmentation. The proposed modifications to an existing hybrid model architecture replace the BiLSTM with standard and residual LSTM, along with convolutional networks, supplemented by data flipping augmentation to replicate the context awareness typically provided by BiLSTM networks. The results demonstrate that the residual LSTM (ResLSTM) model achieves superior performance while maintaining a lower computational complexity compared to the traditional BiLSTM model. Specifically, on the UCI-HAR dataset, the ResLSTM model attains an accuracy of 96.34% with 576,702 parameters, outperforming the BiLSTM model’s accuracy of 95.22% with 849,534 parameters. On the WISDM dataset, the ResLSTM achieves an accuracy of 97.20% with 192,238 parameters, compared to the BiLSTM’s 97.23% accuracy with 283,182 parameters, demonstrating a more efficient architecture with minimal performance trade-off. For the KU-HAR dataset, the ResLSTM model achieves an accuracy of 97.05% with 386,038 parameters, showing comparable performance to the BiLSTM model’s 98.63% accuracy with 569,462 parameters, but with significantly fewer parameters. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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18 pages, 1493 KiB

Open AccessArticle

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection

by Moritz Schneider, Kevin Reich, Ulrich Hartmann, Ingo Hermanns, Mirko Kaufmann, Annette Kluge, Armin Fiedler, Udo Frese and Rolf Ellegast

Sensors 2024, 24(16), 5381; https://doi.org/10.3390/s24165381 - 20 Aug 2024

Viewed by 1116

Abstract

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in [...] Read more.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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18 pages, 8371 KiB

Open AccessArticle

A Novel Method for Identifying Frailty and Quantifying Muscle Strength Using the Six-Minute Walking Test

by Yunjin Zhang, Minoru Morita, Tsunahiko Hirano, Keiko Doi, Xin Han, Kazuto Matsunaga and Zhongwei Jiang

Sensors 2024, 24(14), 4489; https://doi.org/10.3390/s24144489 - 11 Jul 2024

Viewed by 1828

Abstract

The six-minute walking test (6MWT) is an essential test for evaluating exercise tolerance in many respiratory and cardiovascular diseases. Frailty and sarcopenia can cause rapid aging of the cardiovascular system in elderly people. Early detection and evaluation of frailty and sarcopenia are crucial [...] Read more.

The six-minute walking test (6MWT) is an essential test for evaluating exercise tolerance in many respiratory and cardiovascular diseases. Frailty and sarcopenia can cause rapid aging of the cardiovascular system in elderly people. Early detection and evaluation of frailty and sarcopenia are crucial for determining the treatment method. We aimed to develop a wearable measuring system for the 6MWT and propose a method for identifying frailty and quantifying walking muscle strength (WMS). In this study, 60 elderly participants were asked to wear accelerometers behind their left and right ankles during the 6MWT. The gait data were collected by a computer or smartphone. We proposed a method for analyzing walking performance using the stride length (SL) and step cadence (SC) instead of gait speed directly. Four regions (Range I–IV) were divided by cutoff values of SC = 2.0 [step/s] and SL = 0.6 [m/step] for a quick view of the frail state. There were 62.5% of frail individuals distributed in Range III and 72.4% of non-frail individuals in Range I. A concept of a WMS score was proposed for estimating WMS quantitatively. We found that 62.5% of frail individuals were scored as WMS1 and 41.4% of the non-frail elderly as WMS4. The average walking distances corresponding to WMS1–4 were 207 m, 370 m, 432 m, and 462 m, respectively. The WMS score may be a useful tool for quantitatively estimating sarcopenia or frailty due to reduced cardiopulmonary function. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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11 pages, 2791 KiB

Open AccessArticle

The Validity and Reliability of a Real-Time Biofeedback System for Lumbopelvic Control Training in Baseball Players

by Shiu-Min Wang, Po-Hsien Jiang, Kuei-Yuan Chan and Wei-Li Hsu

Sensors 2024, 24(10), 3060; https://doi.org/10.3390/s24103060 - 11 May 2024

Viewed by 1039

Abstract

Background: This study validates real-time biofeedback for lumbopelvic control training in baseball. The lumbopelvic region is crucial for generating kinetic energy in pitching. Real-time biofeedback enhances training effectiveness and reduces injury risk. The validity and reliability of this system were examined. Purpose: This [...] Read more.

Background: This study validates real-time biofeedback for lumbopelvic control training in baseball. The lumbopelvic region is crucial for generating kinetic energy in pitching. Real-time biofeedback enhances training effectiveness and reduces injury risk. The validity and reliability of this system were examined. Purpose: This study was to investigate the validity and reliability of the real-time biofeedback system for lumbopelvic control training. Methods: Twelve baseball players participated in this study, with data collected in two sessions separated by a week. All participants needed to do the lateral slide exercise and single-leg squat exercise in each session. Pelvic angles detected by the real-time biofeedback system were compared to the three-dimensional motion capture system (VICON) during training sessions. Additionally, pelvic angles measured by the biofeedback system were compared between the two training sessions. Results: The real-time biofeedback system exhibited moderate to strong correlations with VICON in both exercises: lateral slide exercise (r = 0.66–0.88, p < 0.05) and single-leg squat exercise (r = 0.70–0.85, p < 0.05). Good to excellent reliability was observed between the first and second sessions for both exercises: lateral slide exercise (ICC = 0.76–0.97) and single-leg squat exercise (ICC = 0.79–0.90). Conclusions: The real-time biofeedback system for lumbopelvic control training, accurately providing the correct pelvic angle during training, could enhance training effectiveness. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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16 pages, 3948 KiB

Open AccessArticle

Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole

by Partha Sarati Das, Daniella Skaf, Lina Rose, Fatemeh Motaghedi, Tricia Breen Carmichael, Simon Rondeau-Gagné and Mohammed Jalal Ahamed

Sensors 2024, 24(9), 2944; https://doi.org/10.3390/s24092944 - 6 May 2024

Cited by 1 | Viewed by 1950

Abstract

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual’s gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric [...] Read more.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual’s gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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18 pages, 7523 KiB

Open AccessArticle

Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation

by Alexander Jamieson, Laura Murray, Vladimir Stankovic, Lina Stankovic and Arjan Buis

Sensors 2023, 23(19), 8164; https://doi.org/10.3390/s23198164 - 29 Sep 2023

Viewed by 1376

Abstract

This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and [...] Read more.

This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route across a variety of terrains in the vicinity of their homes. Their physical activity data were clustered to extract ‘unique’ groupings in a low-dimension feature space in an unsupervised learning approach, and an algorithm was created to automatically distinguish such activities. After testing three dimensionality reduction methods—namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP)—we selected tSNE due to its performance and stable outputs. Cluster formation of activities via DBSCAN only occurred after the data were reduced to two dimensions via tSNE and contained only samples for a single individual. Additionally, through analysis of the t-SNE plots, appreciable clusters in walking-based activities were only apparent with ground walking and stair ambulation. Through a combination of density-based clustering and analysis of cluster distance and density, a novel algorithm inspired by the t-SNE plots, resulting in three proposed and validated hypotheses, was able to identify cluster formations that arose from ground walking and stair ambulation. Low dimensional clustering of activities has thus been found feasible when analyzing individual sets of data and can currently recognize stair and ground walking ambulation. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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11 pages, 17027 KiB

Open AccessCommunication

Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques

by Giuseppina Monti and Luciano Tarricone

Sensors 2023, 23(18), 7748; https://doi.org/10.3390/s23187748 - 8 Sep 2023

Viewed by 1077

Abstract

In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results [...] Read more.

In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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19 pages, 1671 KiB

Open AccessArticle

Improving Inertial Sensor-Based Activity Recognition in Neurological Populations

by Yunus Celik, M. Fatih Aslan, Kadir Sabanci, Sam Stuart, Wai Lok Woo and Alan Godfrey

Sensors 2022, 22(24), 9891; https://doi.org/10.3390/s22249891 - 15 Dec 2022

Cited by 6 | Viewed by 2547

Abstract

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial [...] Read more.

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson’s disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued. Full article

(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)

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