Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 32506

Share This Special Issue

Special Issue Editors

Dr. Caroline Sunderland

E-Mail Website
Guest Editor

Sport Performance Research Group, Sport, Health and Performance Enhancement (SHAPE) Research Centre, Department of Sport Science, Nottingham Trent University, Nottingham, UK
Interests: team sport performance; GPS for activity profile monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Rachel Malcolm

E-Mail Website
Guest Editor

Sport Performance Research Group, Sport, Health and Performance Enhancement (SHAPE) Research Centre, Department of Sport Science, Nottingham Trent University, Nottingham, UK
Interests: Team sport performance, thermoregulation, cognitive function and neuromuscular function

Dr. Daniele Magistro

E-Mail Website
Guest Editor

Department of Sport Science, School of Science and Technology, Nottingham Trent University, 50 Shakespeare St, Nottingham NG1 4FQ, UK
Interests: ageing; physical activity; behaviour change; physical functioning; cognitive functioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, sensors have been used for the objective measurement of movement, human physiology, and physical activity in a wide variety of settings. These have included sports performance, physical activity, and sedentary behavior research. Participants have ranged from elite athletes to clinical populations and young children to the elderly. This Special Issue aims to amalgamate the research which uses sensors to monitor human activity, behavior, and physiology to demonstrate the variety and impact of sensor research.

We welcome research that employs either wearable or environmental sensors from a range of disciplines. Examples include physiology; sport performance; physical activity; movement behavior; sedentary behavior; and physical behavior. We encourage original articles, reviews, perspectives, and letters.

Dr. Caroline Sunderland
Dr. Rachel Malcolm
Dr. Daniele Magistro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Accelerometry
GPS
Motion tracking
Sensors for activity recognition
Wearable sensors
Environmental sensors
Activity of daily living
Movement behavior
Human tracking

Published Papers (12 papers)

Download All Papers

Research

Jump to: Review

15 pages, 2147 KiB

Open AccessArticle

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

by Maartje M. S. Hendriks, Marije Vos-van der Hulst, Ralf W. J. Weijs, Jaap H. van Lotringen, Alexander C. H. Geurts and Noel L. W. Keijsers

Sensors 2022, 22(21), 8387; https://doi.org/10.3390/s22218387 - 01 Nov 2022

Cited by 6 | Viewed by 1837

Abstract

The aim of this study was to objectively assess and compare gait capacity and gait performance in rehabilitation inpatients with stroke or incomplete spinal cord injury (iSCI) using inertial measurement units (IMUs). We investigated how gait capacity (what someone can do) is related to gait performance (what someone does). Twenty-two inpatients (11 strokes, 11 iSCI) wore ankle positioned IMUs during the daytime to assess gait. Participants completed two circuits to assess gait capacity. These were videotaped to certify the validity of the IMU algorithm. Regression analyses were used to investigate if gait capacity was associated with gait performance (i.e., walking activity and spontaneous gait characteristics beyond therapy time). The ankle positioned IMUs validly assessed the number of steps, walking time, gait speed, and stride length (r ≥ 0.81). The walking activity was strongly (r ≥ 0.76) related to capacity-based gait speed. Maximum spontaneous gait speed and stride length were similar to gait capacity. However, the average spontaneous gait speed was half the capacity-based gait speed. Gait capacity can validly be assessed using IMUs and is strongly related to gait performance in rehabilitation inpatients with neurological disorders. Measuring gait performance with IMUs provides valuable additional information about walking activity and spontaneous gait characteristics to inform about functional recovery. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

18 pages, 2429 KiB

Open AccessArticle

Sensor Verification and Analytical Validation of Algorithms to Measure Gait and Balance and Pronation/Supination in Healthy Volunteers

by Robert Ellis, Peter Kelly, Chengrui Huang, Andrew Pearlmutter and Elena S. Izmailova

Sensors 2022, 22(16), 6275; https://doi.org/10.3390/s22166275 - 20 Aug 2022

Cited by 3 | Viewed by 3099

Abstract

Numerous studies have sought to demonstrate the utility of digital measures of motor function in Parkinson’s disease. Frameworks, such as V3, document digital measure development: technical verification, analytical and clinical validation. We present the results of a study to (1) technically verify accelerometers in an Apple iPhone 8 Plus and ActiGraph GT9X versus an oscillating table and (2) analytically validate software tasks for walking and pronation/supination on the iPhone plus passively detect walking measures with the ActiGraph in healthy volunteers versus human raters. In technical verification, 99.4% of iPhone and 91% of ActiGraph tests show good or excellent agreement versus the oscillating table as the gold standard. For the iPhone software task and algorithms, intraclass correlation coefficients (ICCs) > 0.75 are achieved versus the human raters for measures when walking distance is >10 s and pronation/supination when the arm is rotated more than two times. Passively detected walking start and end time was accurate to approx. 1 s and walking measures were accurate to one unit, e.g., one step. The results suggest that the Apple iPhone and ActiGraph GT9X accelerometers are fit for purpose and that task and passively collected measures are sufficiently analytically valid to assess usability and clinical validity in Parkinson’s patients. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

15 pages, 1327 KiB

Open AccessArticle

Identifying the Effects of Age and Speed on Whole-Body Gait Symmetry by Using a Single Wearable Sensor

by Antonino Casabona, Maria Stella Valle, Giulia Rita Agata Mangano and Matteo Cioni

Sensors 2022, 22(13), 5001; https://doi.org/10.3390/s22135001 - 02 Jul 2022

Cited by 1 | Viewed by 1334

Abstract

Studies on gait symmetry in healthy population have mainly been focused on small range of age categories, neglecting Teenagers (13–18 years old) and Middle-Aged persons (51–60 years old). Moreover, age-related effects on gait symmetry were found only when the symmetry evaluation was based on whole-body acceleration than on spatiotemporal parameters of the gait cycle. Here, we provide a more comprehensive analysis of this issue, using a Symmetry Index (SI) based on whole-body acceleration recorded on individuals aged 6 to 84 years old. Participants wore a single inertial sensor placed on the lower back and walked for 10 m at comfortable, slow and fast speeds. The SI was computed using the coefficient of correlation of whole-body acceleration measured at right and left gait cycles. Young Adults (19–35 years old) and Adults (36–50 years old) showed stable SI over the three speed conditions, while Children (6–12 years old), Teenagers (13–18 years old), Middle-Aged persons and Elderly (61–70 and 71–84 years old) exhibited lower SI values when walking at fast speed. Overall, this study confirms that whole-body gait symmetry is lower in Children and in Elderly persons over 60 years of age, showing, for the first time, that asymmetries appear also during teenage period and in Middle-Aged persons (51–60 years old). Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

17 pages, 1117 KiB

Open AccessArticle

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography

by Gert Mertes, Yuan Long, Zhangdaihong Liu, Yuhui Li, Yang Yang and David A. Clifton

Sensors 2022, 22(9), 3303; https://doi.org/10.3390/s22093303 - 26 Apr 2022

Cited by 6 | Viewed by 1645

Abstract

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening (

102.6

\min

of data). The model achieves an average 10-fold cross-validated AUC of

0.95 \pm 0.02

. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

14 pages, 1201 KiB

Open AccessArticle

Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition

by Florentin Delaine and Gregory Faraut

Sensors 2022, 22(7), 2439; https://doi.org/10.3390/s22072439 - 22 Mar 2022

Cited by 2 | Viewed by 1160

Abstract

Monitoring Activities of Daily Living (ADL) has become a major occupation to respond to the aging population and prevent frailty. To do this, the scientific community is using Machine Learning (ML) techniques to learn the lifestyle habits of people at home. The most-used formalism to represent the behaviour of the inhabitant is the Hidden Markov Model (HMM) or Probabilistic Finite Automata (PFA), where events streams are considered. A common decomposition to design ADL using a mathematical model is Activities–Actions–Events (AAE). In this paper, we propose mathematical criteria to evaluate a priori the performance of these instrumentations for the goals of ADL recognition. We also present a case study to illustrate the use of these criteria. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

11 pages, 306 KiB

Open AccessArticle

Determinants of Different Aspects of Upper-Limb Activity after Stroke

by Bea Essers, Camilla Biering Lundquist, Geert Verheyden and Iris Charlotte Brunner

Sensors 2022, 22(6), 2273; https://doi.org/10.3390/s22062273 - 15 Mar 2022

Cited by 3 | Viewed by 1806

Abstract

We examined factors associated with different aspects of upper-limb (UL) activity in chronic stroke to better understand and improve UL activity in daily life. Three different aspects of UL activity were represented by four sensor measures: (1) contribution to activity according to activity ratio and magnitude ratio, (2) intensity of activity according to bilateral magnitude, and (3) variability of activity according to variation ratio. We combined data from a Belgian and Danish patient cohort (n = 126) and developed four models to determine associated factors for each sensor measure. Results from standard multiple regression show that motor impairment (Fugl–Meyer assessment) accounted for the largest part of the explained variance in all sensor measures (18–61%), with less motor impairment resulting in higher UL activity values (p < 0.001). Higher activity ratio, magnitude ratio, and variation ratio were further explained by having the dominant hand affected (p < 0.007). Bilateral magnitude had the lowest explained variance (adjusted R² = 0.376), and higher values were further associated with being young and female. As motor impairment and biological aspects accounted for only one- to two-thirds of the variance in UL activity, rehabilitation including behavioral strategies might be important to increase the different aspects of UL activity. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

17 pages, 3580 KiB

Open AccessArticle

Reproducibility of an Intraoperative Pressure Sensor in Total Knee Replacement

by Camdon Fary, Dean McKenzie and Richard de Steiger

Sensors 2021, 21(22), 7679; https://doi.org/10.3390/s21227679 - 18 Nov 2021

Cited by 2 | Viewed by 1584

Abstract

Appropriate soft tissue tension in total knee replacement (TKR) is an important factor for a successful outcome. The purpose of our study was to assess both the reproducibility of a modern intraoperative pressure sensor (IOP) and if a surgeon could unconsciously influence measurement. A consecutive series of 80 TKRs were assessed with an IOP between January 2018 and December 2020. In the first scenario, two blinded sequential measurements in 48 patients were taken; in a second scenario, an initial blinded measurement and a subsequent unblinded measurement in 32 patients were taken while looking at the sensor monitor screen. Reproducibility was assessed by intraclass correlation coefficients (ICCs). In the first scenario, the ICC ranged from 0.83 to 0.90, and in the second scenario it ranged from 0.80 to 0.90. All ICCs were 0.80 or higher, indicating reproducibility using a IOP and that a surgeon may not unconsciously influence the measurement. The use of a modern IOP to measure soft tissue tension in TKRs is a reproducible technique. A surgeon observing the measurements while performing IOP may not significantly influence the result. An IOP gives additional information that the surgeon can use to optimize outcomes in TKR. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

24 pages, 4376 KiB

Open AccessArticle

An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition

by Veronika Spieker, Amartya Ganguly, Sami Haddadin and Cristina Piazza

Sensors 2021, 21(21), 7404; https://doi.org/10.3390/s21217404 - 07 Nov 2021

Cited by 1 | Viewed by 2657

Abstract

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

12 pages, 400 KiB

Open AccessArticle

Effect of Changing Match Format from Halves to Quarters on the Performance Characteristics of Male University Field Hockey Players

by Elliot P. Lam, Caroline D. Sunderland, John G. Morris, Laura-Anne M. Furlong, Barry S. Mason and Laura A. Barrett

Sensors 2021, 21(16), 5490; https://doi.org/10.3390/s21165490 - 15 Aug 2021

Cited by 2 | Viewed by 2682

Abstract

The study examined whether the performance characteristics of male university field hockey players differed when the match format was 2 × 35 min halves compared to 2 × 2 × 17.5 min quarters. Thirty-five male university field hockey players (age 21.2 ± 3.0 years, height 1.81 ± 0.07 m, body mass 75.1 ± 8.9 kg), competing at national level in the UK, were monitored over 52 matches played across the 2018–2019 (2 × 35 min halves) and 2019–2020 (2 × 2 × 17.5 min quarters) seasons using 15 Hz Global Positioning System units and heart rate monitors. Total distance, high-speed running distance (≥15.5 km·h⁻¹), accelerations (≥2 m·s⁻¹), decelerations (≤−2 m·s⁻¹), average heart rate and percentage of time spent at >85% of maximum heart rate were recorded during both match formats. Two-level random intercept hierarchal models (Match—level 1, Player—level 2) suggested that the change in format from 2 × 35 min halves (2018–2019 season) to 2 × 2 × 17.5 min quarters (2019–2020 season) resulted in a reduction in total distance and high-speed running distance completed during a match (by 221 m and 120 m, respectively, both p < 0.001). As no significant cross-level interactions were observed (between season and half), the change from 35 min halves to 17.5 min quarters did not attenuate the reduced physical performance evident during the second half of matches (total distance: −235 m less in second half; high-speed running distance: −70 m less in second half; both p < 0.001). Overall, the findings suggest that the change in match format did alter the performance characteristics of male university field hockey players, but the quarter format actually reduced the total distance and high-speed running distance completed during matches, and did not attenuate the reduction in performance seen during the second half of matches. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

17 pages, 2315 KiB

Open AccessArticle

Identification of Human Motion Using Radar Sensor in an Indoor Environment

by Sung-wook Kang, Min-ho Jang and Seongwook Lee

Sensors 2021, 21(7), 2305; https://doi.org/10.3390/s21072305 - 25 Mar 2021

Cited by 17 | Viewed by 5742

Abstract

In this paper, we propose a method of identifying human motions, such as standing, walking, running, and crawling, using a millimeter wave radar sensor. In our method, two signal processing is performed in parallel to identify the human motions. First, the moment at which a person’s motion changes is determined based on the statistical characteristics of the radar signal. Second, a deep learning-based classification algorithm is applied to determine what actions a person is taking. In each of the two signal processing, radar spectrograms containing the characteristics of the distance change over time are used as input. Finally, we evaluate the performance of the proposed method with radar sensor data acquired in an indoor environment. The proposed method can find the moment when the motion changes with an error rate of 3%, and also can classify the action that a person is taking with more than 95% accuracy. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

13 pages, 1174 KiB

Open AccessArticle

Quantifying Step Count and Oxygen Consumption with Portable Technology during the 2-Min Walk Test in People with Lower Limb Amputation

by John D. Smith and Gary Guerra

Sensors 2021, 21(6), 2080; https://doi.org/10.3390/s21062080 - 16 Mar 2021

Cited by 6 | Viewed by 3524

Abstract

Step counts and oxygen consumption have yet to be reported during the 2-min walk test (2MWT) test in persons with lower-limb amputations (LLA). The purpose of this study was to determine step counts and oxygen consumption during the 2MWT in LLA. Thirty-five men and women walked for two minutes as quickly as possible while wearing activity monitors (ActiGraph Link on the wrist (LW) and ankle (LA), Garmin vivofit^®3 on the wrist (VW) and ankle (VA), and a modus StepWatch on the ankle (SA), and a portable oxygen analyzer. The StepWatch on the ankle (SA) and the vivofit3 on the wrist (VW) had the least error and best accuracy of the activity monitors studied. While there were no significant differences in distance walked, oxygen consumption (VO₂) or heart rate (HR) between sexes or level of amputation (p > 0.05), females took significantly more steps than males (p = 0.034), and those with unilateral transfemoral amputations took significantly fewer steps than those with unilateral transtibial amputations (p = 0.023). The VW and SA provided the most accurate step counts among the activity monitors and were not significantly different than hand counts. Oxygen consumption for all participants during the 2MWT was 8.9 ± 2.9 mL/kg/min, which is lower than moderate-intensity activity. While some may argue that steady-state activity has not yet been reached in the 2MWT, it may also be possible participants are not walking as fast as they can, thereby misclassifying their performance to a lower standard. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures

Figure 1

Review

Jump to: Research

25 pages, 4399 KiB

Open AccessReview

Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms

by Jacob P. Kimball, Omer T. Inan, Victor A. Convertino, Sylvain Cardin and Michael N. Sawka

Sensors 2022, 22(2), 442; https://doi.org/10.3390/s22020442 - 07 Jan 2022

Cited by 9 | Viewed by 3415

Abstract

Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions. Full article

(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors)

► Show Figures