scholarly journals Human Activities and Postures Recognition: From Inertial Measurements to Quaternion-Based Approaches

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4058 ◽  
Author(s):  
Zmitri ◽  
Fourati ◽  
Vuillerme
Keyword(s):  
Inertial Sensors ◽  
Magnetic Measurement ◽  
Recognition Task ◽  
Computation Time ◽  
Left Thigh ◽  
K Nearest Neighbors ◽  
Raw Data ◽  
Recognition Process ◽  
Measurement Units ◽  
Inertial Measurements

This paper presents two approaches to assess the effect of the number of inertial sensors and their location placements on recognition of human postures and activities. Inertial and Magnetic Measurement Units (IMMUs)—which consist of a triad of three-axis accelerometer, three-axis gyroscope, and three-axis magnetometer sensors—are used in this work. Five IMMUs are initially used and attached to different body segments. Placements of up to three IMMUs are then considered: back, left foot, and left thigh. The subspace k-nearest neighbors (KNN) classifier is used to achieve the supervised learning process and the recognition task. In a first approach, we feed raw data from three-axis accelerometer and three-axis gyroscope into the classifier without any filtering or pre-processing, unlike what is usually reported in the state-of-the-art where statistical features were computed instead. Results show the efficiency of this method for the recognition of the studied activities and postures. With the proposed algorithm, more than 80% of the activities and postures are correctly classified using one IMMU, placed on the lower back, left thigh, or left foot location, and more than 90% when combining all three placements. In a second approach, we extract attitude, in term of quaternion, from IMMUs in order to more precisely achieve the recognition process. The obtained accuracy results are compared to those obtained when only raw data is exploited. Results show that the use of attitude significantly improves the performance of the classifier, especially for certain specific activities. In that case, it was further shown that using a smaller number of features, with quaternion, in the recognition process leads to a lower computation time and better accuracy.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

Systematic Calibration Method for FOG Inertial Measurement Units

2013 ◽  
Vol 662 ◽  
pp. 717-720 ◽  
Author(s):  
Zhen Yu Zheng ◽  
Yan Bin Gao ◽  
Kun Peng He
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Calibration Method ◽  
State Equation ◽  
Measurement Unit ◽  
Observation System ◽  
Body Frame ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Error Coefficients

As an inertial sensors assembly, the FOG inertial measurement unit (FIMU) must be calibrated before being used. The paper presents a one-time systematic IMU calibration method only using two-axis low precision turntable. First, the detail error model of inertial sensors using defined body frame is established. Then, only velocity taken as observation, system 33 state equation is established including the lever arm effects and nonlinear terms of scale factor error. The turntable experiments verify that the method can identify all the error coefficients of FIMU on low-precision two-axis turntable, after calibration the accuracy of navigation is improved.

Abnormalities Detection of IMU Based on PCA in Motion Monitoring

2012 ◽  
Vol 224 ◽  
pp. 533-538 ◽  
Author(s):  
Jing Zhou ◽  
Steven Su ◽  
Ai Huang Guo ◽  
Wei Dong Chen
Keyword(s):  
Measurement Method ◽  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Principal Component ◽  
Body Sensor Networks ◽  
Remote Measurement ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Multiple Degrees Of Freedom ◽  
Measurement Units

Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking people’s daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotelling’s T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible.

Speech Emotion Analysis of Different Age Groups Using Clustering Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 69-85 ◽  
Author(s):  
Hemanta Kumar Palo ◽  
Mihir Narayan Mohanty ◽  
Mahesh Chandra
Keyword(s):  
Vocal Tract ◽  
Speech Rate ◽  
Age Groups ◽  
Recognition Task ◽  
Computation Time ◽  
Vocal Folds ◽  
Recognition System ◽  
Clustering Techniques ◽  
Fcm Algorithm ◽  
Criminal Investigators

The shape, length, and size of the vocal tract and vocal folds vary with the age of the human being. The variation may be of different age or sickness or some other conditions. Arguably, the features extracted from the utterances for the recognition task may differ for different age group. It complicates further for different emotions. The recognition system demands suitable feature extraction and clustering techniques that can separate their emotional utterances. Psychologists, criminal investigators, professional counselors, law enforcement agencies and a host of other such entities may find such analysis useful. In this article, the emotion study has been evaluated for three different age groups of people using the basic age- dependent features like pitch, speech rate, and log energy. The feature sets have been clustered for different age groups by utilizing K-means and Fuzzy c-means (FCM) algorithm for the boredom, sadness, and anger states. K-means algorithm has outperformed the FCM algorithm in terms of better clustering and lower computation time as the authors' results suggest.

scholarly journals Identifikasi Ekstraksi Fitur untuk Gerakan Tangan dalam Bahasa Isyarat (SIBI) Menggunakan Sensor MYO Armband

2019 ◽  
Vol 19 (1) ◽  
pp. 127-137
Author(s):  
Angga Rahagiyanto
Keyword(s):  
Sign Language ◽  
Cross Validation ◽  
Hand Movements ◽  
Raw Data ◽  
Moment Invariant ◽  
Recognition Process ◽  
Movement Recognition ◽  
Myo Armband ◽  
Different Types ◽  
The Moment

Indonesian: Indonesian SIBI has been widely reviewed by researchers using different types of cameras and sensors. The ultimate goal is to produce a strong, fast and accurate movement recognition process. One that supports talk of movement using sensors on the MYO Armband tool. This paper explains how to use raw data generated from the MYO Armband sensor and extract integration so that it can be used to facilitate complete hand, arm and combination movements in the SIBI sign language dictionary. MYO armband uses five sensors: accelerometer, gyroscope, orientation, euler-orientation and EMG. Each sensor produces data that is different in scale and size. This requires a process to make the data uniform. This study uses the min-max method to normalize any data on the MYO Armband sensor and the Moment Invariant method to extract the vector features of hand movements. Testing is done using sign language Movement statistics both dynamic signals. Testing is done using cross validation.

scholarly journals FES-Induced Cycling in Complete SCI: A Simpler Control Method Based on Inertial Sensors

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4268
Author(s):  
Benoît Sijobert ◽  
Ronan Le Guillou ◽  
Charles Fattal ◽  
Christine Azevedo Coste
Keyword(s):  
Electrical Stimulation ◽  
Control Method ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Setting Time ◽  
Reference Information ◽  
Novel Approach ◽  
Crank Angle ◽  
Measurement Units ◽  
User Friendly

This article introduces a novel approach for a functional electrical stimulation (FES) controller intended for FES-induced cycling based on inertial measurement units (IMUs). This study aims at simplifying the design of electrical stimulation timing patterns while providing a method that can be adapted to different users and devices. In most of studies and commercial devices, the crank angle is used as an input to trigger stimulation onset. We propose instead to use thigh inclination as the reference information to build stimulation timing patterns. The tilting angles of both thighs are estimated from one inertial sensor located above each knee. An IF–THEN rule algorithm detects, online and automatically, the thigh peak angles in order to start and stop the stimulation of quadriceps muscles, depending on these events. One participant with complete paraplegia was included and was able to propel a recumbent trike using the proposed approach after a very short setting time. This new modality opens the way for a simpler and user-friendly method to automatically design FES-induced cycling stimulation patterns, adapted to clinical use, for multiple bike geometries and user morphologies.

scholarly journals What Lies Beneath One’s Feet? Terrain Classification Using Inertial Data of Human Walk

2019 ◽  
Vol 9 (15) ◽  
pp. 3099 ◽  
Author(s):  
Muhammad Zeeshan Ul Hasnain Hashmi ◽  
Qaiser Riaz ◽  
Mehdi Hussain ◽  
Muhammad Shahzad
Keyword(s):  
Inertial Sensors ◽  
Support Vector ◽  
Gait Patterns ◽  
Lower Back ◽  
Segmentation Approach ◽  
Angular Velocities ◽  
Measurement Units ◽  
Normal Human ◽  
Recorded Data ◽  
Validation Strategy

The objective of this study was to investigate if the inertial data collected from normal human walk can be used to reveal the underlying terrain types. For this purpose, we recorded the gait patterns of normal human walk on six different terrain types with variation in hardness and friction using body mounted inertial sensors. We collected accelerations and angular velocities of 40 healthy subjects with two smartphones embedded inertial measurement units (MPU-6500) attached at two different body locations (chest and lower back). The recorded data were segmented with stride based segmentation approach and 194 tempo-spectral features were computed for each stride. We trained two machine learning classifiers, namely random forest and support vector machine, and cross validated the results with 10-fold cross-validation strategy. The classification tasks were performed on indoor–outdoor terrains, hard–soft terrains, and a combination of binary, ternary, quaternary, quinary and senary terrains. From the experimental results, the classification accuracies of 97% and 92% were achieved for indoor–outdoor and hard–soft terrains, respectively. The classification results for binary, ternary, quaternary, quinary and senary class classification were 96%, 94%, 92%, 90%, and 89%, respectively. These results demonstrate that the stride data collected with the low-level signals of a single IMU can be used to train classifiers and predict terrain types with high accuracy. Moreover, the problem at hand can be solved invariant of sensor type and sensor location.

scholarly journals Motion Capture Technology in Industrial Applications: A Systematic Review

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5687
Author(s):  
Matteo Menolotto ◽  
Dimitrios-Sokratis Komaris ◽  
Salvatore Tedesco ◽  
Brendan O’Flynn ◽  
Michael Walsh
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Health And Safety ◽  
Industrial Applications ◽  
Machine Learning Algorithms ◽  
Industrial Processes ◽  
Application Study ◽  
First Choice ◽  
Study Characteristics ◽  
Measurement Units

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

Evaluation of spinal posture during gait with inertial measurement units

2020 ◽  
Vol 234 (10) ◽  
pp. 1094-1105
Author(s):  
Elisa Digo ◽  
Giuseppina Pierro ◽  
Stefano Pastorelli ◽  
Laura Gastaldi
Keyword(s):  
Inertial Sensors ◽  
Axial Rotation ◽  
Rehabilitation Program ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Spinal Posture ◽  
Non Invasive ◽  
Flexion Extension ◽  
Invasive Method ◽  
Measurement Units

The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt–twist method was adopted to evaluate flexion–extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context.

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