3-axis Gyroscope Research Articles

Sensors: future tools for detecting young patient's stress during a dental invasive versus a non-invasive dental treatment-a pilot study.

A reliable tool to visualise children's early stress signs to prevent dental fear development is needed. The aim was to evaluate the commercially available, CE marked, Shimmer3 GSR + unit's ability to indicate for stress as a reaction of fear or pain for a non-invasive dental treatment (NI) and an invasive dental treatment (I). Patients 14-16years old were invited to undergo an oral check-up (NI) or an orthodontic premolar extraction (I), respectively. Digital data, measured via electrodes and optical pulse probe, placed on the wrist and fingers, monitored by the Shimmer3 GSR + unit, was transferred via Bluetooth to the HP-laptop. The observed digital parameters were: heart rate based on photoplethysmography (PPG), galvanic skin response (GSR), and 3-axis gyroscope and accelerometer signals for hand movements. Protocols for patient self-report scales were used: coloured analogue scale for pain intensity, facial analogue scale for the mood, and a dental fear scale. Descriptive statistics was performed. The NI-group: 20 patients, (14.6 ± 0.5years), underwent 20 oral check-ups. The I-group: 14 patients, (15.3 ± 0.5years), underwent 28 premolar extractions. All patients tolerated the Shimmer3 GSR + unit well. The GSR signal increased significantly, at start and during the oral injection, in the I-group. The GSR amplitudes persisted throughout and post the dental injection. No general uniform pattern or high GSR amplitudes were produced regarding NI-group. Considering the limitations of this study, the following conclusions can be made: the invasive treatment resulted in a specific unison GSR pattern, while the non-invasive procedure showed individually scattered GSR reactions. The commercially available CE-marked Shimmer3 GSR + device indicated the patient's stress response triggered by the invasive anaesthetic procedure.

G.A.I.T: gait analysis interactive tool a pipeline for automatic detection of gait events across different motor impairments

We introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson’s disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson’s disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects’ age and motor impairments highlights the versatility of our approach.

An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Asymmetries in Postural Stability and Balance Parameters during Specific Elements in Sub-Elite Rhythmic Gymnasts

Purpose: This study aims to investigate the level of lower-limb functional asymmetries in sub-elite rhythmic gymnasts while performing sport-specific elements, including passe and side passe balance stance. Methods: The analysis focuses on postural stability and balance parameters, measured using Gyko, a 3-axis accelerometer, gyroscope, and magnetometer device. The task was to execute a passe balance and maintain the position for 30 seconds, which was the duration of the test, and then perform the same task with the other leg. The test was repeated for both legs in a side passe balance stance. Sixteen active female sub-elite rhythmic gymnasts participated in the study. Results: The results reveal significant differences in the mean travel velocity in all anatomical planes during the passe elements, indicating asymmetry in postural stability in favor of the non-dominant leg (P≤ .01). Similarly, in the side passe balance test, significant differences were observed in the mediolateral (in favor of the dominant leg) and anteroposterior (in favor of the non-dominant leg) total distances, indicating asymmetry in postural stability (P= .03). However, no significant differences were found in other postural stability parameters, the ellipse (EA) and the total length of the trajectory (Len(D)). Conclusions: Evident asymmetries in movement speed during the passe element and imbalances in weight distribution during the side passe balance test do not appear to compromise overall postural stability considering that asymmetry was not proven in several variables, such as the ellipse or the total length of the trajectory. These findings shed light on the potential impact of asymmetries on performance and injury risk in rhythmic gymnastics, emphasizing the importance of considering sport-specific balance tests.

Head Mounted IMU-Based Driver’s Gaze Zone Estimation Using Machine Learning Algorithm

Predicting the driver’s gaze could be important information in preventing accidents while driving. In this study, machine learning models for estimating the driver’s gaze distraction through head movement data were created and their performance was compared and evaluated. Participants wore glasses-type eye trackers and performed the task of selecting the touch screen buttons while driving. The input variable used in the model was data obtained from a 3-axis accelerometer sensor and a 3-axis gyroscope sensor, and the target variable was eye-gaze data. As a result, it was confirmed that the gaze area could be estimated with a precision, sensitivity, specificity, and F1-score of 72.1%, 72.5%, 66.0%, and 69.3%, respectively, only with the head movement sensing data. The model trained using time-series datasets had higher performance than using non-time series datasets. This study presented one alternative that could be used to determine the driver’s status with an inexpensive sensor.

Activity Recognition Using Different Sensor Modalities and Deep Learning

In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories.

The Derivation of Epigastric Motion to Assess Neonatal Breathing and Sleep: An Exploratory Study.

New non-medical monitors are offered for respiration monitoring of neonates. Epigastric motion during sleep was investigated by means of a wearable tracker in parallel to clinical monitoring. 23 hospitalised neonates ready for discharge. A 3-axes-accelerometer and -gyroscope was placed in a standard epigastric position. Between two routine care rounds signals were recorded in parallel to monitoring of impedance pneumography (IP), ECG and pulse oximetry. Motion signals vs. time charts were evaluated using 10-min episodes and semiquantitatively assigned to breathing signal quality, regular breathing, periodic breathing and confounding artefacts. The results were compared with the impedance pneumographic data. 26 recordings (mean duration: 210 min/infant) were conducted without bradycardia or apnea alarm. The gestational age at birth ranged 28.9 to 41.1 and at recording from 35.6 to 42.3 postmenstrual weeks. Motion patterns of quiet sleep with regular breathing, periodic breathing and active sleep with confounding body movements were found. The longitudinal and transversal gyroscope axes resulted in best signal quality. Periodic breathing was found in up to 80% of episodes and decreased inversely with gestational age showing significantly more periodic breathing in preterm infants. Respiration signals of the gyroscope vs. IP showed a low bias and highly variating frequencies. Standardized motion trackers may detect typical neonatal breathing and body-motion-patterns, that could help to classify neonatal sleep. Respiratory rates can only be determined during quiet sleep.

Machine learning based canine posture estimation using inertial data.

The aim of this study was to design a new canine posture estimation system specifically for working dogs. The system was composed of Inertial Measurement Units (IMUs) that are commercially available, and a supervised learning algorithm which was developed for different behaviours. Three IMUs, each containing a 3-axis accelerometer, gyroscope, and magnetometer, were attached to the dogs' chest, back, and neck. To build and test the model, data were collected during a video-recorded behaviour test where the trainee assistance dogs performed static postures (standing, sitting, lying down) and dynamic activities (walking, body shake). Advanced feature extraction techniques were employed for the first time in this field, including statistical, temporal, and spectral methods. The most important features for posture prediction were chosen using Select K Best with ANOVA F-value. The individual contributions of each IMU, sensor, and feature type were analysed using Select K Best scores and Random Forest feature importance. Results showed that the back and chest IMUs were more important than the neck IMU, and the accelerometers were more important than the gyroscopes. The addition of IMUs to the chest and back of dog harnesses is recommended to improve performance. Additionally, statistical and temporal feature domains were more important than spectral feature domains. Three novel cascade arrangements of Random Forest and Isolation Forest were fitted to the dataset. The best classifier achieved an f1-macro of 0.83 and an f1-weighted of 0.90 for the prediction of the five postures, demonstrating a better performance than previous studies. These results were attributed to the data collection methodology (number of subjects and observations, multiple IMUs, use of common working dog breeds) and novel machine learning techniques (advanced feature extraction, feature selection and modelling arrangements) employed. The dataset and code used are publicly available on Mendeley Data and GitHub, respectively.

지자기센서를 이용하지 않는 6축 IMU 기반의 3차원 관절각 추정용 순환 신경망

Inertial measurement unit (IMU)-based 3D joint angle estimation have a wide range of important applications, among them, in gait analysis and exoskeleton robot control. Conventionally, the joint angle was determined via the estimation of 3D orientation of each body segment using 9-axis IMUs including 3-axis magnetometers. However, a magnetometer is limited by magnetic disturbance in the vicinity of the sensor, which highly affects the accuracy of the joint angle. Accordingly, this study aims to estimate the joint angle using the 6-axis IMU signals composed of a 3-axis accelerometer and a 3-axis gyroscope without a magnetometer. This paper proposes a recurrent neural network (RNN) model, which indirectly utilizes the joint kinematic constraint and thus estimates joint angles based on 6-axis IMUs without using a magnetometer signal. The performance of the proposed model was validated for a mechanical joint and human elbow joint, under magnetically disturbed environments. Experimental results showed that the proposed RNN approach outperformed the conventional approach based on a Kalman filter (KF), i.e., RNN 3.48° vs. KF 10.01° for the mechanical joint and RNN 7.39° vs. KF 21.27° for the elbow joint.

Finite Length Triple Estimation Algorithm and its Application to Gyroscope MEMS Noise Identification

Abstract The noises associated with MEMS measurements can significantly impact their accuracy. The noises characterised by random walk and bias instability errors strictly depend on temperature effects that are difficult to specify during direct measurements. Therefore, the paper aims to estimate the fractional noise dynamics of the stationary MEMS gyroscope based on finite length triple estimation algorithm (FLTEA). The paper deals with the state, order and parameter estimation of fractional order noises originating from the MEMS gyroscope, being part of the popular Inertial Measurement Unit denoted as SparkFun MPU9250. The noise measurements from x, y and z gyroscope axes are identified using a modified triple estimation algorithm (TEA) with finite approximation length. The TEA allows a simultaneous estimation of the state, order and parameter of fractional order systems. Moreover, as it is well-known that the number of samples in fractional difference approximations plays a key role, we try to show the influence of applying the TEA with various approximation length constraints on final estimation results. The validation of finite length TEA in the noise estimation process coming from MEMS gyroscope has been conducted for implementation length reduction achieving 50% of samples needed to estimate the noise with no implementation losses. Additionally, the capabilities of modified TEA in the analysis of fractional constant and variable order systems are confirmed in several numerical examples.

Reducing the Sensitivity of Fabrication Error in the MEMS Butterfly Gyroscope by Adopting an Improved Vibration Beam

Microelectromechanical system (MEMS) butterfly gyroscope is one of the most potential high-precision <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ir=textit{x}$ </tex-math></inline-formula> / <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$textit{y}$ </tex-math></inline-formula> -axis gyroscopes, whose vibration beam as the critical component has a large effect on its performance. This article researched the influence of the spindle azimuth on the vibration beam by building a mathematical model, which found the larger and more robust spindle azimuth to process variation benefited the sensitivity and quadrature error. A novel butterfly gyroscope with a concave vibration beam was proposed. And the effect of its parameters on spindle azimuth was analyzed theoretically, which helped to propose a method for designing the most optimal concave vibration beam. In the presence of the same fabrication error, the robustness of the spindle azimuth to process variation was improved about double theoretically compared to the former vibration beam. The fabrication process and the corresponding control system were designed and conducted. Experiments appeared that the butterfly gyroscope with a concave vibration beam was nearly half in the quadrature error ratio (avg 0.869) than the former (avg 1.669). More importantly, the distribution of the quadrature error in the novel butterfly gyroscopes was more concentrated. Their standard deviation was calculated as 0.292, which was nearly half of that of the oblique beam (0.579). It meant the concave beam appeared to have better robustness to process variation.

Design of Hexacopter Tethered Drone

Abstract: This paper proposed the development of an tethered Drone to be used for application. Drones become essential for various application. The main drawback of the drones is that the time of flight is very less (around 20 minutes).The proposed system aims to provide continuous power supply to the drone by using power supply from ground. The twisting and tangling of wires is another problem, so we have provided a slip ring mechanism to avoid twisting of cable. In this system author used IMU 6DOF (3-axis accelerometer&amp; 3-axis gyroscope) which ensure it smooth movement, graceful motion and trajectory tracing. GPS system and barometric sensor makes it more efficient in autonomous mode. UAVs have limited operational autonomy, determined by their on-board available power. An autonomous power tethered UAV-UGV team couples those two classes through a power linkage. This serves to increase the UAV’s autonomy in terms of flight time, while concurrently the aerial platform’s utility as an eye-in-the-sky can provide the UGV with increased knowledge of its surroundings. Finally, this physical constraint can grant additional benefits at aerial vehicle control level, especially under the potential presence of strong wind gusts. The developed autonomous operation strategy is examined within a simulated scenario with limited prior map knowledge, which includes a number of unmapped challenges.

MEMS based eating and drinking gesture spotting using machine learning techniques

An effective human-computer interaction for collaborative, collective and brilliant computing is provided by the Gesture recognition. Our study uses a single 3-axis accelerometer and 3-axis gyroscope for acquiring the data. Dynamic Time Warping (DTW), time and frequency domain and gesture discrepancy features are extracted from the raw data and these features are subjected to supervised machine learning algorithms such as KNN, SVM, RF and ANN. The system is evaluated depending on the data base of more than 10,000 traces obtained from five subsets. Through this study, we intend to identify eating and drinking gestures and more broadly short activities on real time environment. Many approaches for gesture recognition in specific settings have already been explored and studied using the combined interaction of several sensors. Despite these powerful hypotheses, gesture interpretation is still fragile and often depends on the individual’s positioning relative to the cameras not with the sensors.

Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge.

Heart failure (HF) is a disease related to impaired performance of the heart and is a significant cause of mortality and treatment costs in the world. During its progression, HF causes worsening (decompensation) periods which generally require hospital care. In order to reduce the suffering of the patients and the treatment cost, avoiding unnecessary hospital visits is essential, as hospitalization can be prevented by medication. We have developed a data-collection device that includes a high-quality 3-axis accelerometer and 3-axis gyroscope and a single-lead ECG. This allows gathering ECG synchronized data utilizing seismo- and gyrocardiography (SCG, GCG, jointly mechanocardiography, MCG) and comparing the signals of HF patients in acute decompensation state (hospital admission) and compensated condition (hospital discharge). In the MECHANO-HF study, we gathered data from 20 patients, who each had admission and discharge measurements. In order to avoid overfitting, we used only features developed beforehand and selected features that were not outliers. As a result, we found three important signs indicating the worsening of the disease: an increase in signal RMS (root-mean-square) strength (across SCG and GCG), an increase in the strength of the third heart sound (S3), and a decrease in signal stability around the first heart sound (S1). The best individual feature (S3) alone was able to separate the recordings, giving 85.0% accuracy and 90.9% accuracy regarding all signals and signals with sinus rhythm only, respectively. These observations pave the way to implement solutions for patient self-screening of the HF using serial measurements.

WITHDRAWN: 3 Axis Gyro Accelerometer & Artificial Intelligence based Enhancement of GPS Accuracy

The accuracy of positioning is a problem for location-based applications when using a cheap GPS receiver (GPS). This research, however, provides an alternative literature review on the topic of how to enhance the positioning precision of inexpensive GPS receivers used in real-time navigation. Scientists have developed a method for precisely estimating a vehicle's location based on GPS coordinates (latitude, longitude, time, and velocity) by combining information about the vehicle's heading, its current speed, and the distance between two waypoints. Coordinate translation, as well as checks for invalid data, and utilization of the previously estimated valuable reference point, all contribute to an increase in precision. As such, a GPS simulation can be used to assess the reliability of GPS velocity readings. Thanks to GPS, the researcher can track the driver's location, speed, and acceleration in real time. A driver's propensity for taking chances can be uncovered in a GIS setting. Second, researchers can get high-resolution data on vehicle activity just before an accident, which makes it easier to estimate speeds with less error and bias. In this paper, we present a double-antenna method for improving GPS precision by utilizing both an active antenna and a ground plane antenna. The author has proposed improvement in GPS Receiver Accuracy Using Antenna Optimization Techniques like Ground Plane Enhancement for SNR Improvement along with Sensor Fusion combined with Artificial Intelligence. Sensor Fusion Primarily comprises of 3-Axis Accelerometer With 3-Axis Gyroscope forming an IMU, which along with AI, is used to predict navigation parameters in absence of GPS Signal, and when GPS drop communication with satellite due to any cause, Once GPS signal is lost with any cause then this change in delta latitude and delta longitude will compute by our trained artificial neural network, then according to this interpolation, artificial neural network calculates latitude and longitude.

A High-Performance Tactical-Grade Monolithic Horizontal Dual-Axis MEMS Gyroscope With Off-Plane Coupling Suppression Silicon Gratings

This articledemonstrates the developments toward a high-performance monolithic wheeled structure horizontal dual-axis [pitch ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X-</i> axis) and roll ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</i> -axis)] microelectromechanical system (MEMS) gyroscope based on off-plane coupling suppression silicon gratings. Gyroscopes have been fabricated with a silicon-on-glass fabrication process with vacuum sealed in metal packaging. The featured gyroscope operates in split mode with the bandwidths of two axes devices both of ∼70 Hz. By unitizing two individual active restraining control loops that adjust the quadrature amplitude via differentially biasing the silicon gratings in real time, the off-plane mechanical couplings of the two sense modes are significantly reduced by ∼95 and ∼30 times, which enables ∼25-fold and ∼56-fold improvements in the bias instability (BI) for a typical dual-axis device. Tactical-grade performances are achieved for both the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X-</i> axis and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y-</i> axis gyroscopes with angle random walk of 0.02 deg/√h and 0.045 deg/√h along with in-run BI of 0.26 deg/h and 0.65 deg/h, respectively, without any temperature compensation. The current preliminary results show great potential to achieve navigation grade precision by employing the mode-matching technique in the future. The use of silicon gratings for out-of-plane coupling reduction also provides an effective way to build a high-performance planar monolithic chip inertial measurement unit.

ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals

The problem of Airwriting Recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in Human-Computer Interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this paper, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable Inertial Measurement Unit (IMU) on the wrist as images and then utilizing deep learning-based models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF) and Markov Transition Field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at https://github.com/ayushayt/ImAiR.

Analysis of Inertial Measurement Accuracy using Complementary Filter for MPU6050 Sensor

Inertial can be defined as disinclination to motion, action, or change. The inertia of an object is the propensity to remain at rest or if in motion, stays in motion at a steady speed. MPU6050 is one of the low-cost motion tracking sensors that contain a 3-axis gyroscope and 3-axis accelerometer orientation measurement. It is used to analyse the movement or location of a person in an indoor environment. This research is to analyse the accuracy of the inertial measurement of the MPU 6050 sensor. Next, is to improve the achievable accuracy rate up to 95% using the complementary filter and finally to visualize the results on an IoT platform. This MPU6050 sensor is beneficial to an emergency responder such as the firefighter’s department. The accurate inertial measurement and location will help to detect the movement and the motion of the firefighter during operation, especially in an indoor environment. The sensor will detect and collects the inertial measurement of an emergency responder and transmit the data wirelessly by using ESP8266 NodeMCU. Finally, the results can be viewed on an IoT platform. However, the results obtained from the MPU 6050 sensor is not perfectly accurate as there is noise during the measurement. Therefore, a complementary filter is used and analysed in this research. It is expected that the inertial location’s accuracy could be improved by 95% that will provide a precise movement and location of the firefighter during operation.

Measurement of Canine Ataxic Gait Patterns Using Body-Worn Smartphone Sensor Data.

Ataxia is an impairment of the coordination of movement or the interaction of associated muscles, accompanied by a disturbance of the gait pattern. Diagnosis of this clinical sign, and evaluation of its severity is usually done using subjective scales during neurological examination. In this exploratory study we investigated if inertial sensors in a smart phone (3 axes of accelerometer and 3 axes of gyroscope) can be used to detect ataxia. The setting involved inertial sensor data collected by smartphone placed on the dog's back while walking in a straight line. A total of 770 walking sessions were evaluated comparing the gait of 55 healthy dogs to the one of 23 dogs with ataxia. Different machine learning techniques were used with the K-nearest neighbors technique reaching 95% accuracy in discriminating between a healthy control group and ataxic dogs, indicating potential use for smartphone apps for canine ataxia diagnosis and monitoring of treatment effect.

The Impact of Wearable Electronics in Assessing the Effectiveness of Levodopa Treatment in Parkinson's Disease.

In order to evaluate Parkinson disease patients' response to therapeutic interventions, sources of information are mainly patient reports and clinicians' assessment of motor functions. However, these sources can suffer from patient's subjectivity and from inter/intra rater's score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment. Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification. According to our findings, levodopa-based therapy alters the patient's conditions in general, ameliorating something (e.g., bradykinesia), leaving unchanged others (e.g., tremor), but with poor correlation to the levodopa dose. A technology-based approach can objectively assess levodopa-based therapy effectiveness. Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports.