sensors-logo

Journal Browser

Journal Browser

Special Issue "Sensors for Gait, Human Movement Analysis, and Health Monitoring"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Dr. Marco Iosa
Website
Guest Editor
IRCCS Fondazione Santa Lucia, Rome, Italy
Interests: IRCCS Fondazione Santa Lucia, via Ardeatina 306, 00179, Roma

Special Issue Information

Dear Colleagues,

The instrumentation of human movement analysis consists of sensor-based measurement techniques aimed to objectively describe and quantitatively assess the motor functions and motor abilities of a subject. With the instrumentation of movement analysis, the kinematic and kinetic parameters of human movements can be determined, and musculoskeletal functions can be quantitatively evaluated. This is fundamental for assessing motor behaviors, sport performances, health monitoring, and assessing motor impairments in pathological conditions, including the evaluation of improvements during rehabilitation in a more sensible and objective manner than the ordinal scores of “semi-quantitative” clinical scales. The standard laboratories of human movement analyses, especially those for gait analysis, are composed of multi-camera motion capture systems, force platforms, and electromyographic devices. Thanks to technological advances in the field of motion measurement techniques, it is now possible to measure the kinematics of body segments via wearable inertial sensors, such as accelerometers and gyroscopes, allowing also continuous health monitoring during the activities of daily life. This Special Issue aims to highlight the most recent research regarding sensors and their applications in gait analysis and, more generally, human movement analysis, including in health monitoring.

Contributions are invited from groups active in this field of research.

Dr. Marco Iosa
Guest Editor

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 papers will be 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 2000 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

  • gait analysis
  • locomotion
  • movement assessment
  • motion capture
  • stereophotogrammetry
  • inertial measurement unit
  • health monitoring

Published Papers (13 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton
Sensors 2020, 20(13), 3685; https://doi.org/10.3390/s20133685 - 30 Jun 2020
Abstract
Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable [...] Read more.
Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP r2 values were higher than those of GP. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Gait Analysis in a Box: A System Based on Magnetometer-Free IMUs or Clusters of Optical Markers with Automatic Event Detection
Sensors 2020, 20(12), 3338; https://doi.org/10.3390/s20123338 - 12 Jun 2020
Abstract
Gait analysis based on full-body motion capture technology (MoCap) can be used in rehabilitation to aid in decision making during treatments or therapies. In order to promote the use of MoCap gait analysis based on inertial measurement units (IMUs) or optical technology, it [...] Read more.
Gait analysis based on full-body motion capture technology (MoCap) can be used in rehabilitation to aid in decision making during treatments or therapies. In order to promote the use of MoCap gait analysis based on inertial measurement units (IMUs) or optical technology, it is necessary to overcome certain limitations, such as the need for magnetically controlled environments, which affect IMU systems, or the need for additional instrumentation to detect gait events, which affects IMUs and optical systems. We present a MoCap gait analysis system called Move Human Sensors (MH), which incorporates proposals to overcome both limitations and can be configured via magnetometer-free IMUs (MH-IMU) or clusters of optical markers (MH-OPT). Using a test–retest reliability experiment with thirty-three healthy subjects (20 men and 13 women, 21.7 ± 2.9 years), we determined the reproducibility of both configurations. The assessment confirmed that the proposals performed adequately and allowed us to establish usage considerations. This study aims to enhance gait analysis in daily clinical practice. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Effects of Novel Inverted Rocker Orthoses for First Metatarsophalangeal Joint on Gastrocnemius Muscle Electromyographic Activity during Running: A Cross-Sectional Pilot Study
Sensors 2020, 20(11), 3205; https://doi.org/10.3390/s20113205 - 05 Jun 2020
Abstract
Background: The mobility of the first metatarsophalangeal joint (I MPTJ) has been related to the proper windlass mechanism and the triceps surae during the heel-off phase of running gait; the orthopedic treatment of the I MPTJ restriction has been made with typical Morton [...] Read more.
Background: The mobility of the first metatarsophalangeal joint (I MPTJ) has been related to the proper windlass mechanism and the triceps surae during the heel-off phase of running gait; the orthopedic treatment of the I MPTJ restriction has been made with typical Morton extension orthoses (TMEO). Nowadays it is unclear what effects TMEO or the novel inverted rocker orthoses (NIRO) have on the EMG activity of triceps surae during running. Objective: To compare the TMEO effects versus NIRO on EMG triceps surae on medialis and lateralis gastrocnemius activity during running. Study design: A cross-sectional pilot study. Methods: 21 healthy, recreational runners were enrolled in the present research (mean age 31.41 ± 4.33) to run on a treadmill at 9 km/h using aleatory NIRO of 6 mm, NIRO of 8 mm, TMEO of 6 mm, TMEO of 8 mm, and sports shoes only (SO), while the muscular EMG of medial and lateral gastrocnemius activity during 30 s was recorded. Statistical intraclass correlation coefficient (ICC) to test reliability was calculated and the Wilcoxon test of all five different situations were tested. Results: The reliability of values was almost perfect. Data showed that the gastrocnemius lateralis increased its EMG activity between SO vs. NIRO-8 mm (22.27 ± 2.51 vs. 25.96 ± 4.68 mV, p < 0.05) and SO vs. TMEO-6mm (22.27 ± 2.51 vs. 24.72 ± 5.08 mV, p < 0.05). Regarding gastrocnemius medialis, values showed an EMG notable increase in activity between SO vs. NIRO-6mm (22.93 ± 2.1 vs. 26.44 ± 3.63, p < 0.001), vs. NIRO-8mm (28.89 ± 3.6, p < 0.001), and vs. TMEO-6mm (25.12 ± 3.51, p < 0.05). Conclusions: Both TMEO and NIRO have shown an increased EMG of the lateralis and medialis gastrocnemius muscles activity during a full running cycle gait. Clinicians should take into account the present evidence when they want to treat I MTPJ restriction with orthoses, and consider the inherent triceps surae muscular cost relative to running economy. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters
Sensors 2020, 20(10), 2834; https://doi.org/10.3390/s20102834 - 16 May 2020
Abstract
Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters [...] Read more.
Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Measurement System for Unsupervised Standardized Assessment of Timed “Up & Go” and Five Times Sit to Stand Test in the Community—A Validity Study
Sensors 2020, 20(10), 2824; https://doi.org/10.3390/s20102824 - 15 May 2020
Abstract
Comprehensive and repetitive assessments are needed to detect physical changes in an older population to prevent functional decline at the earliest possible stage and to initiate preventive interventions. Established instruments like the Timed “Up & Go” (TUG) Test and the Sit-to-Stand Test (SST) [...] Read more.
Comprehensive and repetitive assessments are needed to detect physical changes in an older population to prevent functional decline at the earliest possible stage and to initiate preventive interventions. Established instruments like the Timed “Up & Go” (TUG) Test and the Sit-to-Stand Test (SST) require a trained person (e.g., physiotherapist) to assess physical performance. More often, these tests are only applied to a selected group of persons already functionally impaired and not to those who are at potential risk of functional decline. The article introduces the Unsupervised Screening System (USS) for unsupervised self-assessments by older adults and evaluates its validity for the TUG and SST. The USS included ambient and wearable movement sensors to measure the user’s test performance. Sensor datasets of the USS’s light barriers and Inertial Measurement Units (IMU) were analyzed for 91 users aged 73 to 89 years compared to conventional stopwatch measurement. A significant correlation coefficient of 0.89 for the TUG test and of 0.73 for the SST were confirmed among USS’s light barriers. Correspondingly, for the inertial data-based measures, a high and significant correlation of 0.78 for the TUG test and of 0.87 for SST were also found. The USS was a validated and reliable tool to assess TUG and SST. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Global Muscle Coactivation of the Sound Limb in Gait of People with Transfemoral and Transtibial Amputation
Sensors 2020, 20(9), 2543; https://doi.org/10.3390/s20092543 - 29 Apr 2020
Cited by 1
Abstract
The aim of this study was to analyze the effect of the level of amputation and various prosthetic devices on the muscle activation of the sound limb in people with unilateral transfemoral and transtibial amputation. We calculated the global coactivation of 12 muscles [...] Read more.
The aim of this study was to analyze the effect of the level of amputation and various prosthetic devices on the muscle activation of the sound limb in people with unilateral transfemoral and transtibial amputation. We calculated the global coactivation of 12 muscles using the time-varying multimuscle coactivation function method in 37 subjects with unilateral transfemoral amputation (10, 16, and 11 with mechanical, electronic, and bionic prostheses, respectively), 11 subjects with transtibial amputation, and 22 healthy subjects representing the control group. The results highlighted that people with amputation had a global coactivation temporal profile similar to that of healthy subjects. However, amputation increased the level of the simultaneous activation of many muscles during the loading response and push-off phases of the gait cycle and decreased it in the midstance and swing subphases. This increased coactivation probably plays a role in prosthetic gait asymmetry and energy consumption. Furthermore, people with amputation and wearing electronic prosthesis showed lower global coactivation when compared with people wearing mechanical and bionic prostheses. These findings suggest that the global lower limb coactivation behavior can be a useful tool to analyze the motor control strategies adopted and the ability to adapt to the prosthetic device. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Can We Accurately Measure Axial Segment Coordination during Turning Using Inertial Measurement Units (IMUs)?
Sensors 2020, 20(9), 2518; https://doi.org/10.3390/s20092518 - 29 Apr 2020
Abstract
Camera-based 3D motion analysis systems are considered to be the gold standard for movement analysis. However, using such equipment in a clinical setting is prohibitive due to the expense and time-consuming nature of data collection and analysis. Therefore, Inertial Measurement Units (IMUs) have [...] Read more.
Camera-based 3D motion analysis systems are considered to be the gold standard for movement analysis. However, using such equipment in a clinical setting is prohibitive due to the expense and time-consuming nature of data collection and analysis. Therefore, Inertial Measurement Units (IMUs) have been suggested as an alternative to measure movement in clinical settings. One area which is both important and challenging is the assessment of turning kinematics in individuals with movement disorders. This study aimed to validate the use of IMUs in the measurement of turning kinematics in healthy adults compared to a camera-based 3D motion analysis system. Data were collected from twelve participants using a Vicon motion analysis system which were compared with data from four IMUs placed on the forehead, middle thorax, and feet in order to determine accuracy and reliability. The results demonstrated that the IMU sensors produced reliable kinematic measures and showed excellent reliability (ICCs 0.80–0.98) and no significant differences were seen in paired t-tests in all parameters when comparing the two systems. This suggests that the IMU sensors provide a viable alternative to camera-based motion capture that could be used in isolation to gather data from individuals with movement disorders in clinical settings and real-life situations. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Symmetry Analysis of Amputee Gait Based on Body Center of Mass Trajectory and Discrete Fourier Transform
Sensors 2020, 20(8), 2392; https://doi.org/10.3390/s20082392 - 23 Apr 2020
Abstract
The calculation of symmetry in amputee gait is a valuable tool to assess the functional aspects of lower limb prostheses and how it impacts the overall gait mechanics. This paper analyzes the vertical trajectory of the body center of mass (CoM) of a [...] Read more.
The calculation of symmetry in amputee gait is a valuable tool to assess the functional aspects of lower limb prostheses and how it impacts the overall gait mechanics. This paper analyzes the vertical trajectory of the body center of mass (CoM) of a group formed by transfemoral amputees and non-amputees to quantitatively compare the symmetry level of this parameter for both cases. A decomposition of the vertical CoM into discrete Fourier series (DFS) components is performed for each subject’s CoM trajectory to identify the main components of each pattern. A DFS-based index is then calculated to quantify the CoM symmetry level. The obtained results show that the CoM displays different patterns along a gait cycle for each amputee, which differ from the sine-wave shape obtained in the non-amputee case. The CoM magnitude spectrum also reveals more coefficients for the amputee waveforms. The different CoM trajectories found in the studied subjects can be thought as the manifestation of developed compensatory mechanisms, which lead to gait asymmetries. The presence of odd components in the magnitude spectrum is related to the asymmetric behavior of the CoM trajectory, given the fact that this signal is an even function for a non-amputee gait. The DFS-based index reflects this fact due to the high value obtained for the non-amputee reference, in comparison to the low values for each amputee. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Stronger Association between High Intensity Physical Activity and Cardiometabolic Health with Improved Assessment of the Full Intensity Range Using Accelerometry
Sensors 2020, 20(4), 1118; https://doi.org/10.3390/s20041118 - 18 Feb 2020
Abstract
An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to better capture variations in physical activity intensity in a lab setting. The aim of the study was to [...] Read more.
An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to better capture variations in physical activity intensity in a lab setting. The aim of the study was to investigate how this improved measure of physical activity affected the relationship with markers of cardiometabolic health. Accelerometer data and markers of cardiometabolic health from 725 adults from two samples, LIV 2013 and SCAPIS pilot, were analyzed. The accelerometer data was processed using both the original ActiGraph method with a low-pass cut-off at 1.6 Hz and the improved method with a low-pass cut-off at 10 Hz. The relationship between the physical activity intensity spectrum and a cardiometabolic health composite score was investigated using partial least squares regression. The strongest association between physical activity and cardiometabolic health was shifted towards higher intensities with the 10 Hz output compared to the ActiGraph method. In addition, the total explained variance was higher with the improved method. The 10 Hz output enables correctly measuring and interpreting high intensity physical activity and shows that physical activity at this intensity is stronger related to cardiometabolic health compared to the most commonly used ActiGraph method. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System
Sensors 2020, 20(1), 125; https://doi.org/10.3390/s20010125 - 24 Dec 2019
Cited by 1
Abstract
Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and [...] Read more.
Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Open AccessArticle
Human Motion Recognition of Knitted Flexible Sensor in Walking Cycle
Sensors 2020, 20(1), 35; https://doi.org/10.3390/s20010035 - 19 Dec 2019
Abstract
Knitted fabric sensors have been widely used as strain sensors in the sports health field and its large strain performance and structure are suitable for human body movements. When a knitted structure is worn, different human body movements are reflected through the large [...] Read more.
Knitted fabric sensors have been widely used as strain sensors in the sports health field and its large strain performance and structure are suitable for human body movements. When a knitted structure is worn, different human body movements are reflected through the large strain deformation of fabric structure and consequently change the electrical signal. Here, the mechanical and electrical properties of highly elastic knitted sweatpants were tested under large strain. This sensor has good sensitivity and stability during movement. Compared with traditional motion monitoring, this technique divides the walking cycle into two stages, namely, stance and swing phases, which can be further subdivided into six stages. The corresponding resistance characteristic values can accurately distinguish the gait cycle. Analysis on hysteresis and repeatability revealed that the sensor exhibits a constant electrical performance. Four kinds of motion postures were predicted and judged by comparing the resistance characteristic range value, peak value calculation function and time axis. The measured sensor outputs were transferred to a computer via 4.0 Bluetooth. Matlab language was used to detect the status through a rule-based algorithm and the sensor outputs. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Graphical abstract

Open AccessArticle
Flexible Insole Sensors with Stably Connected Electrodes for Gait Phase Detection
Sensors 2019, 19(23), 5197; https://doi.org/10.3390/s19235197 - 27 Nov 2019
Cited by 2
Abstract
Gait analysis is an important assessment tool for analyzing vital signals collected from individuals and for providing physical information of the human body, and it is emerging in a diverse range of application scenarios, such as disease diagnosis, fall prevention, rehabilitation, and human–robot [...] Read more.
Gait analysis is an important assessment tool for analyzing vital signals collected from individuals and for providing physical information of the human body, and it is emerging in a diverse range of application scenarios, such as disease diagnosis, fall prevention, rehabilitation, and human–robot interaction. Herein, a kind of surface processed conductive rubber was designed and investigated to develop a pressure-sensitive insole to monitor planar pressure in a real-time manner. Due to a novel surface processing method, the pressure sensor was characterized by stable contact resistance, simple manufacturing, and high mechanical durability. In the experiments, it was demonstrated that the developed pressure sensors were easily assembled with the inkjet-printed electrodes and a flexible substrate as a pressure-sensitive insole while maintaining good sensing performance. Moreover, resistive signals were wirelessly transmitted to computers in real time. By analyzing sampled resistive data combined with the gait information monitored by a visual-based reference system based on machine learning method (k-Nearest Neighbor algorithm), the corresponding relationship between plantar pressure distribution and lower limb joint angles was obtained. Finally, the experimental validation of the ability to accurately divide gait into several phases was conducted, illustrating the potential application of the developed device in healthcare and robotics. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Biofeedback Systems for Gait Rehabilitation of Individuals with Lower-Limb Amputation: A Systematic Review
Sensors 2020, 20(6), 1628; https://doi.org/10.3390/s20061628 - 14 Mar 2020
Abstract
Individuals with lower-limb amputation often have gait deficits and diminished mobility function. Biofeedback systems have the potential to improve gait rehabilitation outcomes. Research on biofeedback has steadily increased in recent decades, representing the growing interest toward this topic. This systematic review highlights the [...] Read more.
Individuals with lower-limb amputation often have gait deficits and diminished mobility function. Biofeedback systems have the potential to improve gait rehabilitation outcomes. Research on biofeedback has steadily increased in recent decades, representing the growing interest toward this topic. This systematic review highlights the methodological designs, main technical and clinical challenges, and evidence relating to the effectiveness of biofeedback systems for gait rehabilitation. This review provides insights for developing an effective, robust, and user-friendly wearable biofeedback system. The literature search was conducted on six databases and 31 full-text articles were included in this review. Most studies found biofeedback to be effective in improving gait. Biofeedback was most commonly concurrently provided and related to limb loading and symmetry ratios for stance or step time. Visual feedback was the most used modality, followed by auditory and haptic. Biofeedback must not be obtrusive and ideally provide a level of enjoyment to the user. Biofeedback appears to be most effective during the early stages of rehabilitation but presents some usability challenges when applied to the elderly. More research is needed on younger populations and higher amputation levels, understanding retention as well as the relationship between training intensity and performance. Full article
(This article belongs to the Special Issue Sensors for Gait, Human Movement Analysis, and Health Monitoring)
Show Figures

Figure 1

Back to TopTop