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

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 63654

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Special Issue Editors

Dr. Frédéric Dierick

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Guest Editor

1. Laboratoire d'Analyse du Mouvement et de la Posture, Centre National De Rééducation Fonctionnelle et de Réadaptation - Rehazenter, 2674 Luxembourg, Luxembourg
2. Faculty of Motor Sciences, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
3. CeREF-Technique, Chaussée de Binche 159, 7000 Mons, Hainaut, Belgium
Interests: motion analysis; rehabilitation; sensors in medicine and health care

Dr. Fabien Buisseret

E-Mail Website
Guest Editor

1. CeREF, Chaussée de Binche 159, 7000 Mons, Belgium
2. Service de Physique Nucléaire et Subnucléaire, Université de Mons, UMONS Research Institute for Complex Systems, 20 Place du Parc, 7000 Mons, Belgium
Interests: theoretical physics; hadrons; mechanics; fractal analysis; motion analysis; kinematics; modelling complex systems; biomechanics
Special Issues, Collections and Topics in MDPI journals

Dr. Stéphanie Eggermont

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Guest Editor

CeREF-Technique, Chaussée de Binche 159, 7000 Mons, Hainaut, Belgium
Interests: embedded plateform, e-health, power electronic, sensors

Special Issue Information

Dear Colleagues,

The electrical, chemical, and mechanical activities that occur during various biological events produce signals that can be measured by sensors, followed by storage and subsequent analysis. These biosignals contain valuable information that can be used to understand the underlying physiological mechanisms of specific biological functions, such as blood pressure, body temperature, joint movement, and the electrical activity of the brain, heart, and muscles. Thus, they may be crucial for clinicians and medical diagnosis.

In the last few years, a great variety of sensors dedicated to biosignals have become available at prices typically lower than 100 USD. These sensors are known as “low-cost sensors”, in contrast to the gold-standard materials used in clinical environments and research centers. For example, movement of a joint can be tracked using low-cost 3D cameras, such as Microsoft’s Kinect v2. The use of such sensors in daily clinical practice may greatly favor the collection of big data and allow broader diffusion of evidence-based medicine, which is essential to improve medical practice. Low-cost sensors may also be of interest in virtual- or augmented-reality medical and rehabilitation applications.

However, the use of low-cost sensors is associated with several challenges. Firstly, sensors should be accurate enough to unambiguously compute relevant indicators from biosignals, in particular, in patients with medical conditions. Secondly, the designed sensors should be as non-intrusive and ready-to-use as possible with fast calibration procedures. Third, they require user-friendly and cross-platform interfaces that provide secure data storage and easy data analysis and visualization.

Authors are invited to submit articles to this Special Issue of Sensors “Low-Cost Sensors and Biological Signals” on, but not limited to, the following topics:

Clinical applications of low-cost sensors;
Metrological comparison between low-cost sensors and gold-standard sensors;
Calibration methods;
Signal processing (including deep-learning techniques);
Data storage and/or wireless transmission;
Real-time data processing and visualisation;
Use of sensors in rehabilitation (biofeedback, virtual reality, augmented reality, etc.);
Sensor design and noninvasive measurement techniques;
Ethical and epistemological dimensions of sensor-based medicine.

The Guest Editors thank the full support from the European Regional Development Fund (Interreg FWVl NOMADe) so as to be guest editors of this special issue, especially the financial support of some publications in this special issue.

Dr. Frédéric Dierick
Dr. Fabien Buisseret
Dr. Stéphanie Eggermont
Guest Editors

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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

low-cost sensors
metrology
motion analysis
virtual reality
signal processing
physiological signals
detection of pathologies
biofeedback
rehabilitation

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Published Papers (13 papers)

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Editorial

Jump to: Research, Review

3 pages, 194 KiB

Open AccessEditorial

Low-Cost Sensors and Biological Signals

by Frédéric Dierick, Fabien Buisseret and Stéphanie Eggermont

Sensors 2021, 21(4), 1482; https://doi.org/10.3390/s21041482 - 20 Feb 2021

Cited by 2 | Viewed by 2678

Abstract

Low-cost sensors, i [...] Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

Research

Jump to: Editorial, Review

15 pages, 1499 KiB

Open AccessArticle

The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching

by Matthew H. Foreman and Jack R. Engsberg

Sensors 2020, 20(24), 7073; https://doi.org/10.3390/s20247073 - 10 Dec 2020

Cited by 6 | Viewed by 2366

Abstract

Compensatory movements at the trunk are commonly utilized during reaching by persons with motor impairments due to neurological injury such as stroke. Recent low-cost motion sensors may be able to measure trunk compensation, but their validity and reliability for this application are unknown. The purpose of this study was to compare the first (K1) and second (K2) generations of the Microsoft Kinect to a video motion capture system (VMC) for measuring trunk compensation during reaching. Healthy participants (n = 5) performed reaching movements designed to simulate trunk compensation in three different directions and on two different days while being measured by all three sensors simultaneously. Kinematic variables related to reaching range of motion (ROM), planar reach distance, trunk flexion and lateral flexion, shoulder flexion and lateral flexion, and elbow flexion were calculated. Validity and reliability were analyzed using repeated-measures ANOVA, paired t-tests, Pearson’s correlations, and Bland-Altman limits of agreement. Results show that the K2 was closer in magnitude to the VMC, more valid, and more reliable for measuring trunk flexion and lateral flexion during extended reaches than the K1. Both sensors were highly valid and reliable for reaching ROM, planar reach distance, and elbow flexion for all conditions. Results for shoulder flexion and abduction were mixed. The K2 was more valid and reliable for measuring trunk compensation during reaching and therefore might be prioritized for future development applications. Future analyses should include a more heterogeneous clinical population such as persons with chronic hemiparetic stroke. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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17 pages, 20802 KiB

Open AccessFeature PaperArticle

Low-Complexity Design and Validation of Wireless Motion Sensor Node to Support Physiotherapy

by Jona Cappelle, Laura Monteyne, Jarne Van Mulders, Sarah Goossens, Maarten Vergauwen and Liesbet Van der Perre

Sensors 2020, 20(21), 6362; https://doi.org/10.3390/s20216362 - 7 Nov 2020

Cited by 9 | Viewed by 4346

Abstract

We present a motion sensor node to support physiotherapy, based on an Inertial Measurement Unit (IMU). The node has wireless interfaces for both data exchange and charging, and is built based on commodity components. It hence provides an affordable solution with a low threshold to technology adoption. We share the hardware design and explain the calibration and validation procedures. The sensor node has an autonomy of 28 h in operation and a standby time of 8 months. On-device sensor fusion yields static results of on average

3.28

° with a drift of 2° per half hour. The final prototype weighs 38 g and measures ø6 cm ×

1.5

cm. The resulting motion sensor node presents an easy to use device for both live monitoring of movements as well as interpreting the data afterward. It opens opportunities to support and follow up treatment in medical cabinets as well as remotely. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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23 pages, 4324 KiB

Open AccessArticle

Differences in Motion Accuracy of Baduanjin between Novice and Senior Students on Inertial Sensor Measurement Systems

by Hai Li, Selina Khoo and Hwa Jen Yap

Sensors 2020, 20(21), 6258; https://doi.org/10.3390/s20216258 - 2 Nov 2020

Cited by 8 | Viewed by 2981

Abstract

This study aimed to evaluate the motion accuracy of novice and senior students in Baduanjin (a traditional Chinese sport) using an inertial sensor measurement system (IMU). Study participants were nine novice students, 11 senior students, and a teacher. The motion data of all participants were measured three times with the IMU. Using the motions of the teacher as the standard motions, we used dynamic time warping to calculate the distances between the motion data of the students and the teacher to evaluate the motion accuracy of the students. The distances between the motion data of the novice students and the teacher were higher than that between senior students and the teacher (p < 0.05 or p < 0.01). These initial results showed that the IMU and the corresponding mathematical methods could effectively distinguish the differences in motion accuracy between novice and senior students of Baduanjin. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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32 pages, 8305 KiB

Open AccessArticle

Biosensing and Actuation—Platforms Coupling Body Input-Output Modalities for Affective Technologies

by Miquel Alfaras, William Primett, Muhammad Umair, Charles Windlin, Pavel Karpashevich, Niaz Chalabianloo, Dionne Bowie, Corina Sas, Pedro Sanches, Kristina Höök, Cem Ersoy and Hugo Gamboa

Sensors 2020, 20(21), 5968; https://doi.org/10.3390/s20215968 - 22 Oct 2020

Cited by 20 | Viewed by 4435

Abstract

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer’s felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of “orchestration”. By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users’ body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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10 pages, 716 KiB

Open AccessArticle

Variability of Coordination in Typically Developing Children Versus Children with Autism Spectrum Disorder with and without Rhythmic Signal

by Lidia V. Gabis, Shahar Shefer and Sigal Portnoy

Sensors 2020, 20(10), 2769; https://doi.org/10.3390/s20102769 - 13 May 2020

Cited by 3 | Viewed by 2334

Abstract

Motor coordination deficit is a cardinal feature of autism spectrum disorder (ASD). The evaluation of coordination of children with ASD is either lengthy, subjective (via observational analysis), or requires cumbersome post analysis. We therefore aimed to use tri-axial accelerometers to compare inter-limb coordination measures between typically developed (TD) children and children ASD, while jumping with and without a rhythmic signal. Children aged 5–6 years were recruited to the ASD group (n = 9) and the TD group (n = 19). Four sensors were strapped to their ankles and wrist and they performed at least eight consecutive jumping jacks twice: at a self-selected rhythm and with a metronome. The primary outcome measures were the timing lag (TL), the timing difference of the maximal acceleration of the left and right limbs, and the lag variability (LV), the variation of TL across the 5 jumps. The LV of the legs of children with ASD was higher compared to the LV of the legs of TD children during self-selected rhythm jumping (p < 0.01). Additionally, the LV of the arms of children with ASD, jumping with the rhythmic signal, was higher compared to that of the TD children (p < 0.05). There were no between-group differences in the TL parameter. Our preliminary findings suggest that the simple protocol presented in this study might allow an objective and accurate quantification of the intra-subject variability of children with ASD via actigraphy. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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18 pages, 2289 KiB

Open AccessArticle

SteadEye-Head—Improving MARG-Sensor Based Head Orientation Measurements Through Eye Tracking Data

by Lukas Wöhle and Marion Gebhard

Sensors 2020, 20(10), 2759; https://doi.org/10.3390/s20102759 - 12 May 2020

Cited by 13 | Viewed by 4051

Abstract

This paper presents the use of eye tracking data in Magnetic AngularRate Gravity (MARG)-sensor based head orientation estimation. The approach presented here can be deployed in any motion measurement that includes MARG and eye tracking sensors (e.g., rehabilitation robotics or medical diagnostics). The challenge in these mostly indoor applications is the presence of magnetic field disturbances at the location of the MARG-sensor. In this work, eye tracking data (visual fixations) are used to enable zero orientation change updates in the MARG-sensor data fusion chain. The approach is based on a MARG-sensor data fusion filter, an online visual fixation detection algorithm as well as a dynamic angular rate threshold estimation for low latency and adaptive head motion noise parameterization. In this work we use an adaptation of Madgwicks gradient descent filter for MARG-sensor data fusion, but the approach could be used with any other data fusion process. The presented approach does not rely on additional stationary or local environmental references and is therefore self-contained. The proposed system is benchmarked against a Qualisys motion capture system, a gold standard in human motion analysis, showing improved heading accuracy for the MARG-sensor data fusion up to a factor of 0.5 while magnetic disturbance is present. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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11 pages, 1879 KiB

Open AccessArticle

DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding

by Tal Krasovsky, Patrice L. Weiss, Oren Zuckerman, Avihay Bar, Tal Keren-Capelovitch and Jason Friedman

Sensors 2020, 20(7), 2114; https://doi.org/10.3390/s20072114 - 9 Apr 2020

Cited by 5 | Viewed by 3920

Abstract

Clinically feasible assessment of self-feeding is important for adults and children with motor impairments such as stroke or cerebral palsy. However, no validated assessment tool for self-feeding kinematics exists. This work presents an initial validation of an instrumented spoon (DataSpoon) developed as an evaluation tool for self-feeding kinematics. Ten young, healthy adults (three male; age 27.2 ± 6.6 years) used DataSpoon at three movement speeds (slow, comfortable, fast) and with three different grips: “natural”, power and rotated power grip. Movement kinematics were recorded concurrently using DataSpoon and a magnetic motion capture system (trakSTAR). Eating events were automatically identified for both systems and kinematic measures were extracted from yaw, pitch and roll (YPR) data as well as from acceleration and tangential velocity profiles. Two-way, mixed model Intraclass correlation coefficients (ICC) and 95% limits of agreement (LOA) were computed to determine agreement between the systems for each kinematic variable. Most variables demonstrated fair to excellent agreement. Agreement for measures of duration, pitch and roll exceeded 0.8 (excellent agreement) for >80% of speed and grip conditions, whereas lower agreement (ICC < 0.46) was measured for tangential velocity and acceleration. A bias of 0.01–0.07 s (95% LOA [−0.54, 0.53] to [−0.63, 0.48]) was calculated for measures of duration. DataSpoon enables automatic detection of self-feeding using simple, affordable movement sensors. Using movement kinematics, variables associated with self-feeding can be identified and aid clinical reasoning for adults and children with motor impairments. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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16 pages, 4397 KiB

Open AccessArticle

Photoplethysmographic Time-Domain Heart Rate Measurement Algorithm for Resource-Constrained Wearable Devices and its Implementation

by Marek Wójcikowski and Bogdan Pankiewicz

Sensors 2020, 20(6), 1783; https://doi.org/10.3390/s20061783 - 23 Mar 2020

Cited by 32 | Viewed by 5604

Abstract

This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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25 pages, 8774 KiB

Open AccessArticle

A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

by Anas M. Tahir, Muhammad E. H. Chowdhury, Amith Khandakar, Sara Al-Hamouz, Merna Abdalla, Sara Awadallah, Mamun Bin Ibne Reaz and Nasser Al-Emadi

Sensors 2020, 20(4), 957; https://doi.org/10.3390/s20040957 - 11 Feb 2020

Cited by 85 | Viewed by 11193

Abstract

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles’ features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects’ gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles’ sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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15 pages, 6611 KiB

Open AccessArticle

DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head’s Rotational Kinematics in Adults during the Didren-Laser Test

by Renaud Hage, Christine Detrembleur, Frédéric Dierick, Laurent Pitance, Laurent Jojczyk, Wesley Estievenart and Fabien Buisseret

Sensors 2020, 20(3), 833; https://doi.org/10.3390/s20030833 - 4 Feb 2020

Cited by 14 | Viewed by 4528

Abstract

Various noninvasive measurement devices can be used to assess cervical motion. The size, complexity, and cost of gold-standard systems make them not suited to clinical practice, and actually difficult to use outside a dedicated laboratory. Nowadays, ultra-low-cost inertial measurement units are available, but without any packaging or a user-friendly interface. The so-called DYSKIMOT is a home-designed, small-sized, motion sensor based on the latter technology, aiming at being used by clinicians in “real-life situations”. DYSKIMOT was compared with a gold-standard optoelectronic system (Elite). Our goal was to evaluate the DYSKIMOT accuracy in assessing fast head rotations kinematics. Kinematics was simultaneously recorded by systems during the execution of the DidRen Laser test and performed by 15 participants and nine patients. Kinematic variables were computed from the position, speed and acceleration time series. Two-way ANOVA, Passing–Bablok regressions, and dynamic time warping analysis showed good to excellent agreement between Elite and DYSKIMOT, both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events’ measured values. In conclusion, DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients, demonstrating its usefulness in both clinical practice and research environments. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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17 pages, 2917 KiB

Open AccessArticle

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

by Julien Lebleu, Thierry Gosseye, Christine Detrembleur, Philippe Mahaudens, Olivier Cartiaux and Massimo Penta

Sensors 2020, 20(3), 715; https://doi.org/10.3390/s20030715 - 28 Jan 2020

Cited by 36 | Viewed by 5873

Abstract

Inertial measurement unit (IMU) records of human movement can be converted into joint angles using a sensor-to-segment calibration, also called functional calibration. This study aims to compare the accuracy and reproducibility of four functional calibration procedures for the 3D tracking of the lower limb joint angles of young healthy individuals in gait. Three methods based on segment rotations and one on segment accelerations were used to compare IMU records with an optical system for their accuracy and reproducibility. The squat functional calibration movement, offering a low range of motion of the shank, provided the least accurate measurements. A comparable accuracy was obtained in other methods with a root mean square error below 3.6° and an absolute difference in amplitude below 3.4°. The reproducibility was excellent in the sagittal plane (intra-class correlation coefficient (ICC) > 0.91, standard error of measurement (SEM) < 1.1°), good to excellent in the transverse plane (ICC > 0.87, SEM < 1.1°), and good in the frontal plane (ICC > 0.63, SEM < 1.2°). The better accuracy for proximal joints in calibration movements using segment rotations was traded to distal joints in calibration movements using segment accelerations. These results encourage further applications of IMU systems in unconstrained rehabilitative contexts. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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Review

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20 pages, 2295 KiB

Open AccessReview

Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

by Andrea Catherine Alarcón-Aldana, Mauro Callejas-Cuervo and Antonio Padilha Lanari Bo

Sensors 2020, 20(21), 5989; https://doi.org/10.3390/s20215989 - 22 Oct 2020

Cited by 55 | Viewed by 7864

Abstract

The use of videogames and motion capture systems in rehabilitation contributes to the recovery of the patient. This systematic review aimed to explore the works related to these technologies. The PRISMA method (Preferred Reporting Items for Systematic reviews and Meta-Analyses) was used to search the databases Scopus, PubMed, IEEE Xplore, and Web of Science, taking into consideration four aspects: physical rehabilitation, the use of videogames, motion capture technologies, and upper limb rehabilitation. The literature selection was limited to open access works published between 2015 and 2020, obtaining 19 articles that met the inclusion criteria. The works reported the use of inertial measurement units (37%), a Kinect sensor (48%), and other technologies (15%). It was identified that 26% used commercial products, while 74% were developed independently. Another finding was that 47% of the works focus on post-stroke motor recovery. Finally, diverse studies sought to support physical rehabilitation using motion capture systems incorporating inertial units, which offer precision and accessibility at a low cost. There is a clear need to continue generating proposals that confront the challenges of rehabilitation with technologies which offer precision and healthcare coverage, and which, additionally, integrate elements that foster the patient’s motivation and participation. Full article

(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)

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