scholarly journals A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4008 ◽  
Author(s):  
Henry Griffith ◽  
Yan Shi ◽  
Subir Biswas
Keyword(s):  
Regression Models ◽  
Large Scale ◽  
Inertial Sensors ◽  
Mutual Influence ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Percentage Error ◽  
Support Vector ◽  
Accelerometer Sensor ◽  
Fill Level

Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink volume and the current fill level on the resulting motion pattern, along with differences in biomechanics across individuals. While motion-based strategies are a promising approach due to the proliferation of inertial sensors, previous studies have been characterized by limited accuracy and substantial variability in performance across subjects. This research seeks to address these limitations for a container-attachable triaxial accelerometer sensor. Drink volume is computed using support vector machine regression models with hand-engineered features describing the container’s estimated inclination. Results are presented for a large-scale data collection consisting of 1908 drinks consumed from a refillable bottle by 84 individuals. Per-drink mean absolute percentage error is reduced by 11.05% versus previous state-of-the-art results for a single wrist-wearable inertial measurement unit (IMU) sensor assessed using a similar experimental protocol. Estimates of aggregate consumption are also improved versus previously reported results for an attachable sensor architecture. An alternative tracking approach using the fill level from which a drink is consumed is also explored herein. Fill level regression models are shown to exhibit improved accuracy and reduced inter-subject variability versus volume estimators. A technique for segmenting the entire drink motion sequence into transport and sip phases is also assessed, along with a multi-target framework for addressing the known interdependence of volume and fill level on the resulting drink motion signature.

A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

2013 ◽  
Vol 117 (1188) ◽  
pp. 111-132 ◽  
Author(s):  
T. L. Grigorie ◽  
R. M. Botez
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Minimum Variance ◽  
Measurement Unit ◽  
Time Step ◽  
Accelerometer Sensor ◽  
Sensor Model ◽  
Input Acceleration

Abstract This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions. A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal. To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

Quantifying Throw Counts and Intensities Throughout a Season in Youth Baseball Players: A Pilot Study

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Michael J. Rose ◽  
Katherine A. McCollum ◽  
Michael T. Freehill ◽  
Stephen M. Cain
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Study Data ◽  
Measurement Unit ◽  
Support Vector ◽  
Identification Algorithm ◽  
Machine Model ◽  
Baseball Players ◽  
Youth Baseball ◽  
Baseball Team

Abstract Overuse injuries in youth baseball players due to throwing are at an all-time high. Traditional methods of tracking player throwing load only count in-game pitches and therefore leave many throws unaccounted for. Miniature wearable inertial sensors can be used to capture motion data outside of the lab in a field setting. The objective of this study was to develop a protocol and algorithms to detect throws and classify throw intensity in youth baseball athletes using a single, upper arm-mounted inertial sensor. Eleven participants from a youth baseball team were recruited to participate in the study. Each participant was given an inertial measurement unit (IMU) and was instructed to wear the sensor during any baseball activity for the duration of a summer season of baseball. A throw identification algorithm was developed using data from a controlled data collection trial. In this report, we present the throw identification algorithm used to identify over 17,000 throws during the 2-month duration of the study. Data from a second controlled experiment were used to build a support vector machine model to classify throw intensity. Using this classification algorithm, throws from all participants were classified as being “low,” “medium,” or “high” intensity. The results demonstrate that there is value in using sensors to count every throw an athlete makes when assessing throwing load, not just in-game pitches.

scholarly journals Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 412
Author(s):  
Luigi Borzì ◽  
Ivan Mazzetta ◽  
Alessandro Zampogna ◽  
Antonio Suppa ◽  
Fernanda Irrera ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Regression Model ◽  
Support Vector Regression ◽  
Regression Models ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Support Vector ◽  
Model Parameters ◽  
The Impact

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

scholarly journals Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 570 ◽  
Author(s):  
Volkan Senyurek ◽  
Masudul Imtiaz ◽  
Prajakta Belsare ◽  
Stephen Tiffany ◽  
Edward Sazonov
Keyword(s):  
Cigarette Smoking ◽  
Inertial Sensor ◽  
Smoking Behavior ◽  
Measurement Unit ◽  
Support Vector ◽  
Svm Classifier ◽  
Free Living ◽  
Objective Monitoring ◽  
In The Wild ◽  
Detection Of Smoking

In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT2.0) and proposes a method that integrates information from an instrumented lighter and a 6-axis Inertial Measurement Unit (IMU) on the wrist for accurate detection of smoking events. The PACT2.0 was utilized in a study of 35 moderate to heavy smokers in both controlled (1.5–2 h) and unconstrained free-living conditions (~24 h). The collected dataset contained approximately 871 h of IMU data, 463 lighting events, and 443 cigarettes. The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures (HMG) in the IMU data by a Support Vector Machine (SVM) classifier. The leave-one-subject-out (LOSO) cross-validation on the data from the controlled portion of the study achieved high accuracy and F1-score of smoking event detection and estimation of puff counts (97%/98% and 93%/86%, respectively). The results of validation in free-living demonstrate 84.9% agreement with self-reported cigarettes. These results suggest that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions.

Research on the Taxonomy of Activity Recognition Based on Inertial Sensors

2013 ◽  
Vol 823 ◽  
pp. 107-110
Author(s):  
Zi Ming Xiao ◽  
Yu Long Shi ◽  
Yong Xue ◽  
Feng Hu ◽  
Yu Chuan Wu
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Future Research ◽  
Support Vector ◽  
Bayesian Decision ◽  
Classification Techniques ◽  
Vector Machines ◽  
Bayesian Decision Making

This paper introduces some techniques on classifying human activities with inertial sensors and point out a number of characteristics of classification algorithm. The goal of human activity recognition is to automatically analyze ongoing activities from people who wear inertial sensor. Initially, we provide introduce information about the activity recognition, such as the way of acquisition, sensors used and the steps of activity recognition using machine learning algorithm. Next, we focus on the classification techniques together with a detailed taxonomy, and the classification techniques implemented and compared in this study are: Decision Tree Algorithm (DTA), Bayesian Decision Making (BDM), Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Hidden Markov Model (HMM)[. Finally, we make a summarize about it investigate the directions for future research.

scholarly journals Wineinformatics: Regression on the Grade and Price of Wines through Their Sensory Attributes

Fermentation ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 84 ◽  
Author(s):  
James Palmer ◽  
Bernard Chen
Keyword(s):  
Regression Models ◽  
Large Scale ◽  
Support Vector ◽  
Prior Work ◽  
Point Scale ◽  
Large Dataset ◽  
Advantages And Disadvantages ◽  
Large Scale Dataset ◽  
Actual Grade ◽  
Classification And Regression

Wineinformatics is a field that uses machine-learning and data-mining techniques to glean useful information from wine. In this work, attributes extracted from a large dataset of over 100,000 wine reviews are used to make predictions on two variables: quality based on a “100-point scale”, and price per 750 mL bottle. These predictions were built using support vector regression. Several evaluation metrics were used for model evaluation. In addition, these regression models were compared to classification accuracies achieved in a prior work. When regression was used for classification, the results were somewhat poor; however, this was expected since the main purpose of the regression was not to classify the wines. Therefore, this paper also compares the advantages and disadvantages of both classification and regression. Regression models can successfully predict within a few points of the correct grade of a wine. On average, the model was only 1.6 points away from the actual grade and off by about $13 per bottle of wine. To the best of our knowledge, this is the first work to use a large-scale dataset of wine reviews to perform regression predictions on grade and price.

Wireless Input Technology Based on MEMS Inertial Measurement Unit - A Survey

2017 ◽  
Vol 870 ◽  
pp. 79-84
Author(s):  
Zhen Xian Fu ◽  
Guang Ying Zhang ◽  
Yu Rong Lin ◽  
Yang Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Propagation Model ◽  
Measurement Unit ◽  
Input Device ◽  
Operational Environment ◽  
Inertial Measurement ◽  
Mems Imu

Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

scholarly journals Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3127
Author(s):  
Giuseppe Loprencipe ◽  
Flavio Guilherme Vaz de Almeida Filho ◽  
Rafael Henrique de Oliveira ◽  
Salvatore Bruno
Keyword(s):  
High Performance ◽  
Ride Comfort ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Road Networks ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
Urban Road ◽  
Comfort Index

Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system (GPS) module, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to indirectly monitor the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was used. Considering 21 km of roads with different levels of pavement decay, validation measurements were performed using the novel sensor, a high performance inertial based navigation sensor, and a road surface profiler. Therefore, comparisons between awz determined with accelerations measured on the two different inertial sensors are made; in addition, also correlations between awz, and typical pavement indicators such as international roughness index, and ride number were also performed. The results showed very good correlations between the awz values calculated with the two inertial devices (R2 = 0.98). In addition, the correlations between awz values and the typical pavement indices showed promising results (R2 = 0.83–0.90). The proposed sensor may be assumed as a reliable and easy-to-install method to assess the pavement conditions in urban road networks, since the use of traditional systems is difficult and/or expensive.

scholarly journals A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6689
Author(s):  
Shibaprasad Bhattacharya ◽  
Kanak Kalita ◽  
Robert Čep ◽  
Shankar Chakraborty
Keyword(s):  
Composite Materials ◽  
Regression Models ◽  
Polynomial Regression ◽  
Prediction Performance ◽  
Wilcoxon Test ◽  
Percentage Error ◽  
Support Vector ◽  
Machining Processes ◽  
Median Regression ◽  
Input Parameters

Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ridge regression, lasso regression and elastic net regression are explored in accurately predicting response values during turning and drilling operations of composite materials. Their prediction performance is also contrasted using four statistical metrics, i.e., mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error and root relative squared error. Based on the lower values of those metrics and Friedman rank and aligned rank tests, SVR emerges out as the best performing model, whereas the prediction performance of median regression is worst. The results of the Wilcoxon test based on the drilling dataset identify the existence of statistically significant differences between the performances of LR and PCR, and PR and median regression models.

scholarly journals Feature Selection for Work Recognition and Working Motion Measurement

2018 ◽  
Vol 30 (5) ◽  
pp. 706-716 ◽  
Author(s):  
Saori Miyajima ◽  
Takayuki Tanaka ◽  
Natsuki Miyata ◽  
Mitsunori Tada ◽  
Masaaki Mochimaru ◽  
...  
Keyword(s):  
Feature Selection ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Feature Selection Method ◽  
The Body ◽  
Support Vector ◽  
Body Parts ◽  
Care Services ◽  
Physical Burden ◽  
New Feature

As the demand for nursing care services is growing, the physical burden involved in caregiving has drawn widespread attention. To mitigate the physical burden in caregiving, we have to recognize what kind of work and problems are involved in each caregiving task. To identify the problems involved in caregiving, we need to recognize the work and analyze its workload. Aiming to reduce the burden on the waist during caregiving tasks, we are developing inertial sensor suits for measuring the working motions. With the developed method, the burden on the waist is estimated from the waist posture. Considering its use in practical caregiving sites, the number of inertial sensors should be the minimum necessary, which depends on the number of body parts where to measure the posture. In this study, we select the body parts to achieve the two above-mentioned goals: to recognize the work involved in caregiving and capture the waist posture. A support vector machine (SVM) is used to recognize the work. Its conventional method of selecting the features on which to recognize the work only considers the recognition accuracy and does not sufficiently meet the needs for measuring the postures. Therefore, we propose a new feature-selection method, which can evaluate the waist-posture measuring accuracy and can make forward feature selections in the same manner as the conventional wrapper method. We have verified the effectiveness of the proposed method by measuring simple simulated work motions.

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