Integration of Camera and Inertial Measurement Unit for Entire Human Robot Interaction Using Machine Learning Algorithm

2019 ◽  
Author(s):  
Azam Haghighi ◽  
Mohamad Bdiwi ◽  
Matthias Putz
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Learning Algorithm ◽  
Human Robot Interaction ◽  
Measurement Unit ◽  
Machine Learning Algorithm ◽  
Robot Interaction ◽  
Inertial Measurement

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Development of an Automatic Functional Movement Screening System with Inertial Measurement Unit Sensors

2020 ◽  
Vol 11 (1) ◽  
pp. 96
Author(s):  
Wen-Lan Wu ◽  
Meng-Hua Lee ◽  
Hsiu-Tao Hsu ◽  
Wen-Hsien Ho ◽  
Jing-Min Liang
Keyword(s):  
Feature Selection ◽  
Inertial Measurement Unit ◽  
Learning Algorithm ◽  
Kappa Statistic ◽  
Weighted Kappa ◽  
Classification Model ◽  
Measurement Unit ◽  
Functional Movement ◽  
Inertial Measurement ◽  
Movement Screening

Background: In this study, an automatic scoring system for the functional movement screen (FMS) was developed. Methods: Thirty healthy adults fitted with full-body inertial measurement unit sensors completed six FMS exercises. The system recorded kinematics data, and a professional athletic trainer graded each participant. To reduce the number of input variables for the predictive model, ordinal logistic regression was used for subset feature selection. The ensemble learning algorithm AdaBoost.M1 was used to construct classifiers. Accuracy and F score were used for classification model evaluation. The consistency between automatic and manual scoring was assessed using a weighted kappa statistic. Results: When all the features were used, the predict model presented moderate to high accuracy, with kappa values between fair to very good agreement. After feature selection, model accuracy decreased about 10%, with kappa values between poor to moderate agreement. Conclusions: The results indicate that higher prediction accuracy was achieved using the full feature set compared with using the reduced feature set.

scholarly journals Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

2019 ◽  
Vol 6 ◽  
pp. 205566831986854 ◽  
Author(s):  
Rob Argent ◽  
Sean Drummond ◽  
Alexandria Remus ◽  
Martin O’Reilly ◽  
Brian Caulfield
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Joint Angle ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Reference Standard ◽  
Inertial Measurement ◽  
Knee Joint Angle ◽  
Single Sensor

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

Exploring Machine Learning Models Based on Accelerometer Sensor Alone or Combined With Gyroscope to Classify Home-Based Exercises and Physical Behavior in (Pre)sarcopenic Older Adults

2021 ◽  
pp. 1-13
Author(s):  
Lenore Dedeyne ◽  
Jorgen A. Wullems ◽  
Jolan Dupont ◽  
Jos Tournoy ◽  
Evelien Gielen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Learning Models ◽  
Top Down ◽  
Inertial Measurement ◽  
Physical Behavior ◽  
Home Based ◽  
Machine Learning Models

Tools for objective monitoring of home-based training and physical behavior (PB) in (pre)sarcopenic older adults are needed. The present study explored two approaches with machine learning models, including accelerometer data either with or without additional gyroscope data (in an inertial measurement unit). Twenty-five community-dwelling (pre)sarcopenic adults mean 80.7 [5.5] years) performed the Otago exercise protocol (OEP) and PB modules (e.g., sitting or walking) while wearing an inertial measurement unit on the lower back (Dynaport MoveMonitor; McRoberts, The Hague, The Netherlands). Machine learning (ML) models for classification were developed by two approaches (top-down and bottom-up approaches) and with two levels of classification: general (no wear, OEP, and PB) and detailed (all subclassifications). Classification output was compared with video recordings. For the bottom-up approach, one ML model was developed. For the top-down approach, data were first classified as no wear, OEP, or PB. Thereafter, OEP and PB were subclassified by one ML model each into subclassification. Only classification of the general classification no wear and the subclassification sitting/lying reached the acceptable performance threshold of 80%. This result was independent of the approach used. Moreover, a gyroscope did not improve performance. Despite the progress in ML techniques and monitors, objective compliance registrations remain challenging.

scholarly journals Machine Learning to Improve Orientation Estimation in Sports Situations Challenging for Inertial Sensor Use

2021 ◽  
Vol 3 ◽  
Author(s):  
Marit P. van Dijk ◽  
Manon Kok ◽  
Monique A. M. Berger ◽  
Marco J. M. Hoozemans ◽  
DirkJan H. E. J. Veeger
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Body Segment ◽  
Inertial Measurement ◽  
Wheelchair Sports ◽  
Trunk Inclination ◽  
Better Than

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7° root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.

scholarly journals Accurate Heuristic Terrain Prediction in Powered Lower-Limb Prostheses Using Onboard Sensors

2020 ◽  
Author(s):  
Roman Stolyarov ◽  
Matt Carney ◽  
Hugh Herr
Keyword(s):  
Machine Learning ◽  
Ankle Joint ◽  
Heuristic Algorithm ◽  
Lower Limb ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Measurement Unit ◽  
Machine Learning Algorithm ◽  
Ground Slope ◽  
Powered Prosthesis

This study describes the development and offline validation of a heuristic algorithm for accurate prediction of ground terrain in a lower limb prosthesis. This method is based on inference of the ground terrain geometry using estimation of prosthetic limb kinematics during gait with a single integrated inertial measurement unit. We asked five subjects with below-knee amputations to traverse level ground, stairs, and ramps using a high-range-of-motion powered prosthesis while internal sensor data were remotely logged. We used these data to develop two terrain prediction algorithms. The first employed a state-of-the-art machine learning approach, while the second was a directly tuned heuristic using thresholds on estimated prosthetic ankle joint translations and ground slope. We compared the performance of these algorithms using resubstitution error for the machine learning algorithm and overall error for the heuristic algorithm. Our optimal machine learning algorithm attained a resubstitution error of $3.4\%$ using 45 features, while our heuristic method attained an overall prediction error of $2.8\%$ using only 5 features derived from estimation of ground slope and horizontal and vertical ankle joint displacement. Compared with pattern recognition, the heuristic performed better on each individual subject, and across both level and non-level strides. These results demonstrate a method for heuristic prediction of ground terrain in a powered prosthesis. The method is more accurate, more interpretable, and less computationally expensive than state-of-the-art machine learning methods, and relies only on integrated prosthesis sensors. Finally, the method provides intuitively tunable thresholds to improve performance for specific walking conditions.

scholarly journals Segmentation of shoulder rehabilitation exercises for single and multiple inertial sensor systems

2020 ◽  
Vol 7 ◽  
pp. 205566832091537
Author(s):  
Louise Brennan ◽  
Antonio Bevilacqua ◽  
Tahar Kechadi ◽  
Brian Caulfield
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Accuracy Score ◽  
Sensor Location ◽  
Inertial Measurement ◽  
Usability Problems ◽  
Segmentation Methods ◽  
Single Sensor ◽  
Rehabilitation Exercises

Introduction Digital home rehabilitation systems require accurate segmentation methods to provide appropriate feedback on repetition counting and exercise technique. Current segmentation methods are not suitable for clinical use; they are not highly accurate or require multiple sensors, which creates usability problems. We propose a model for accurately segmenting inertial measurement unit data for shoulder rehabilitation exercises. This study aims to use inertial measurement unit data to train and test a machine learning segmentation model for single- and multiple-inertial measurement unit systems and to identify the optimal single-sensor location. Methods A focus group of specialist physiotherapists selected the exercises, which were performed by participants wearing inertial measurement units on the wrist, arm and scapula. We applied a novel machine learning based segmentation technique involving a convolutional classifier and Finite State Machine to the inertial measurement unit data. An accuracy score was calculated for each possible single- or multiple-sensor system. Results The wrist inertial measurement unit was chosen as the optimal single-sensor location for future system development (mean overall accuracy 0.871). Flexion and abduction based exercises mostly could be segmented with high accuracy, but scapular movement exercises had poor accuracy. Conclusion A wrist-worn single inertial measurement unit system can accurately segment shoulder exercise repetitions; however, accuracy varies depending on characteristics of the exercise.

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