Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass

Detection of hoof-on and -off events are essential to gait classification in horses. Wearable sensors have been endorsed as a convenient alternative to the traditional force plate-based method. The aim of this study was to propose and validate inertial sensor-based methods of gait event detection, reviewing different sensor locations and their performance on different gaits and exercise surfaces. Eleven horses of various breeds and ages were recruited to wear inertial sensors attached to the hooves, pasterns and cannons. Gait events detected by pastern and cannon methods were compared to the reference, hoof-detected events. Walk and trot strides were recorded on asphalt, grass and sand. Pastern-based methods were found to be the most accurate and precise for detecting gait events, incurring mean errors of between 1 and 6ms, depending on the limb and gait, on asphalt. These methods incurred consistent errors when used to measure stance durations on all surfaces, with mean errors of 0.1 to 1.16% of a stride cycle. In conclusion, the methods developed and validated here will enable future studies to reliably detect equine gait events using inertial sensors, under a wide variety of field conditions.

Download Full-text

An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors

Sensors ◽

10.3390/s19112644 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2644

Author(s):

Vladislava Bobić ◽

Milica Djurić-Jovičić ◽

Nataša Dragašević ◽

Mirjana B. Popović ◽

Vladimir S. Kostić ◽

...

Keyword(s):

Clinical Practice ◽

Decision Support ◽

Expert System ◽

Inertial Sensors ◽

Wearable Sensors ◽

Objective Evaluation ◽

Finger Tapping ◽

Symptom Evaluation ◽

Wearable Inertial Sensors ◽

Processing Techniques

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson’s disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.

Download Full-text

Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Sensors ◽

10.3390/s20010082 ◽

2019 ◽

Vol 20 (1) ◽

pp. 82 ◽

Cited By ~ 1

Author(s):

Udeni Jayasinghe ◽

William S. Harwin ◽

Faustina Hwang

Keyword(s):

Inertial Sensors ◽

Inertial Sensor ◽

Wearable Sensors ◽

Correlation Coefficients ◽

Movement Analysis ◽

Multiple Sensors ◽

Sensor Orientation ◽

Multiple Devices ◽

Good Potential ◽

Sensor Errors

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

Download Full-text

An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

IoT ◽

10.3390/iot1020021 ◽

2020 ◽

Vol 1 (2) ◽

pp. 360-381

Author(s):

Matthew T. O. Worsey ◽

Hugo G. Espinosa ◽

Jonathan B. Shepherd ◽

David V. Thiel

Keyword(s):

Machine Learning ◽

Prediction Accuracy ◽

Inertial Sensors ◽

Inertial Sensor ◽

Supervised Machine Learning ◽

Learning Models ◽

Significant Statistical Difference ◽

Sensor Configuration ◽

Wearable Inertial Sensors ◽

Machine Learning Models

Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing.

Download Full-text

Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

Journal of Healthcare Engineering ◽

10.1155/2020/7914649 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Abeer A. Badawi ◽

Ahmad Al-Kabbany ◽

Heba A. Shaban

Keyword(s):

Feature Selection ◽

Inertial Sensors ◽

Inertial Sensor ◽

Real Life ◽

Human Gait ◽

Body Sensor Networks ◽

Sequential Feature Selection ◽

Wearable Inertial Sensors ◽

Robust Systems ◽

Remote Health

This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.

Download Full-text

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Sensors ◽

10.3390/s20092660 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2660 ◽

Cited By ~ 2

Author(s):

Fabio Alexander Storm ◽

Ambra Cesareo ◽

Gianluigi Reni ◽

Emilia Biffi

Keyword(s):

Inertial Sensors ◽

Inertial Sensor ◽

Pathological Condition ◽

Low Cost ◽

Walk Test ◽

Female Ratio ◽

Number Of Patients ◽

Wearable Inertial Sensors ◽

6 Minute Walk Test ◽

Minute Walk

Wearable sensors are becoming increasingly popular for complementing classical clinical assessments of gait deficits. The aim of this review is to examine the existing knowledge by systematically reviewing a large number of papers focusing on the use of wearable inertial sensors for the assessment of gait during the 6-minute walk test (6MWT), a widely recognized, simple, non-invasive, low-cost and reproducible exercise test. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 28 full-text articles. Then, the available knowledge was summarized regarding study design, subjects enrolled (number of patients and pathological condition, if any, age, male/female ratio), sensor characteristics (type, number, sampling frequency, range) and body placement, 6MWT protocol and extracted parameters. Results were critically discussed to suggest future directions for the use of inertial sensor devices in the clinics.

Download Full-text

Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Sensors ◽

10.3390/s19194075 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4075 ◽

Cited By ~ 25

Author(s):

Marco Ghislieri ◽

Laura Gastaldi ◽

Stefano Pastorelli ◽

Shigeru Tadano ◽

Valentina Agostini

Keyword(s):

Inertial Sensors ◽

Postural Sway ◽

Wearable Sensors ◽

Critical Discussion ◽

Standing Balance ◽

Wearable Systems ◽

The Subject ◽

Wearable Inertial Sensors ◽

Selection Of

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen’s kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., “active aging”, biofeedback-based rehabilitation for fall prevention, and the management of Parkinson’s disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

Download Full-text

Using Inertial Sensor System to Measure the Workspace of the Surgeon's Upper Limbs during Operations

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.772.329 ◽

2015 ◽

Vol 772 ◽

pp. 329-333

Author(s):

Ali Soroush ◽

Farzam Farahmand

Keyword(s):

Upper Limb ◽

Inertial Sensors ◽

Inertial Sensor ◽

Kinematic Model ◽

Experimental Results ◽

Upper Body ◽

Upper Limbs ◽

Surgical Robots ◽

Raw Data ◽

Wearable Inertial Sensors

The aim of this study was to determine the workspace of surgeon's body for designing more efficient surgical robots in the operation rooms. Five wearable inertial sensors were placed near the wrist and elbow joints and also on the thorax of surgeons to track the orientation of upper limb. Assuming that the lengths of five segments of an upper limb were known, measurements of the inertial sensors were used to determine the position of the wrist and elbow joints via an established kinematic model. subsequently, to assess the workspace of surgeon upper body, raw data were collected in the arthroscopy and laparoscopy operations. Experimental results demonstrated that the workspaces of surgeon's joints are limited and predefined. The results can be used for designing surgical robots and surgeon body supports.

Download Full-text

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Sensors ◽

10.3390/s20247143 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7143 ◽

Cited By ~ 1

Author(s):

Dylan Kobsar ◽

Zaryan Masood ◽

Heba Khan ◽

Noha Khalil ◽

Marium Yossri Kiwan ◽

...

Keyword(s):

Gait Analysis ◽

Scoping Review ◽

Inertial Sensors ◽

Inertial Sensor ◽

Linear Acceleration ◽

Patient Specific ◽

Common Location ◽

Spatiotemporal Parameters ◽

Wearable Inertial Sensors ◽

Study Designs

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

Download Full-text

Wearable inertial sensors and pressure MAT detect risk factors associated with ACL graft failure that are not possible with traditional return to sport assessments

BMJ Open Sport & Exercise Medicine ◽

10.1136/bmjsem-2019-000557 ◽

2019 ◽

Vol 5 (1) ◽

pp. e000557 ◽

Cited By ~ 2

Author(s):

Michael J Dan ◽

Kimberly Kai Lun ◽

Luke Dan ◽

Jimmy Efird ◽

Matthew Pelletier ◽

...

Keyword(s):

Graft Failure ◽

Assessment Tool ◽

Inertial Sensors ◽

Inertial Sensor ◽

Elite Athletes ◽

Cruciate Ligament ◽

Force Plate ◽

Return To Sport ◽

Thigh Circumference ◽

Pressure Plate

IntroductionAnterior cruciate ligament reconstruction (ACLR) is associated with poor return to sport and high graft re-rupture rates. This study explored the use of a wearable inertial sensor (ViMove) that incorporates an accelerometer and gyroscope, and MatScan pressure sensing mat (TekScan, South Boston, Massachusetts, USA) to provide objective return-to-sport measures.MethodsThree cohorts’ ACLR patients, non-athletic controls and elite athletes (Australian seven’s rugby Olympic Gold medallist). Patients performed biometric and functional tests (thigh circumference and triple hop) and the ViMove knee module (consisting of single and double leg squats, hops and box drops) for lower limb alignment assessment, concurrently with force plate.ResultsElite athletes had less varus/valgus (VV) movement during ViMove exercises compared with the ACLR cohort, who in turn had less VV malalignment than controls. When analysing side-to-side differences, single leg squats and box drop were asymmetrical in the ACL group, with greater malalignment in the reconstructed leg (p<0.05). Subgroup analysis failed to differentiate who passed or failed current return to sport assessment. TekScan pressure plate detected differences in double leg landing and flight time while hopping not detected with ViMove, suggesting ACL patients compensate by offloading the reconstructed leg to improve coronal alignment during double leg activity.ConclusionThe inertial sensor detected differences in motion for patients following ACLR, which are known to be associated with graft rupture and were not detected with functional return to sport testing. Coupling the device with data from a pressure plate provides a powerful assessment tool detecting alignment differences known to be associate with graft failure only previously detected in formal gait analysis.

Download Full-text

Exploring Risk of Falls and Dynamic Unbalance in Cerebellar Ataxia by Inertial Sensor Assessment

Sensors ◽

10.3390/s19245571 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5571 ◽

Cited By ~ 3

Author(s):

Pietro Caliandro ◽

Carmela Conte ◽

Chiara Iacovelli ◽

Antonella Tatarelli ◽

Stefano Filippo Castiglia ◽

...

Keyword(s):

Cerebellar Ataxia ◽

Gait Cycle ◽

Inertial Sensors ◽

Inertial Sensor ◽

Step Length ◽

Clinical Severity ◽

Variable Step ◽

Risk Of Falls ◽

Wearable Inertial Sensors ◽

Dynamic Unbalance

Background. Patients suffering from cerebellar ataxia have extremely variable gait kinematic features. We investigated whether and how wearable inertial sensors can describe the gait kinematic features among ataxic patients. Methods. We enrolled 17 patients and 16 matched control subjects. We acquired data by means of an inertial sensor attached to an ergonomic belt around pelvis, which was connected to a portable computer via Bluetooth. Recordings of all the patients were obtained during overground walking. From the accelerometric data, we obtained the harmonic ratio (HR), i.e., a measure of the acceleration patterns, smoothness and rhythm, and the step length coefficient of variation (CV), which evaluates the variability of the gait cycle. Results. Compared to controls, patients had a lower HR, meaning a less harmonic and rhythmic acceleration pattern of the trunk, and a higher step length CV, indicating a more variable step length. Both HR and step length CV showed a high effect size in distinguishing patients and controls (p < 0.001 and p = 0.011, respectively). A positive correlation was found between the step length CV and both the number of falls (R = 0.672; p = 0.003) and the clinical severity (ICARS: R = 0.494; p = 0.044; SARA: R = 0.680; p = 0.003). Conclusion. These findings demonstrate that the use of inertial sensors is effective in evaluating gait and balance impairment among ataxic patients.

Download Full-text