Study of Feature Extraction Techniques for Sensor Data Classification

2021 ◽  
Vol 13 (1) ◽  
pp. 33-46
Author(s):  
Anupama Jawale ◽  
Ganesh Magar
Keyword(s):  
Research Study ◽  
Binary Classification ◽  
Fall Detection ◽  
The Elderly ◽  
Sensor Data ◽  
Classification Algorithms ◽  
Accelerometer Data ◽  
Extraction Techniques ◽  
Selection Of ◽  
Statistical Functions

Human activity recognition is a rapidly growing area in healthcare systems. The applications include fall detection, ambiguous activity, dangerous behavior, etc. It has become one of the important requirements for the elderly or neurological disorder patients. The devices included are accelerometer and gyroscope, which generate large amounts of data. Accuracy of classification algorithms for this data is highly dependent upon extraction and selection of data features. This research study has extracted time domain features, based on statistical functions as well as rotational features around three axes. Gyroscope data features are also used to enhance accuracy of accelerometer data. Three popular classification techniques are used to classify the accelerometer dataset into activity categories. Binary classification (run -1 / walk-0) is considered. The results have shown SVM and LDA when used with rotation and gyroscope data gives the highest accuracy of 92.0% whereas FDA shows 84% accuracy.

scholarly journals SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3363 ◽  
Author(s):  
Taylor Mauldin ◽  
Marc Canby ◽  
Vangelis Metsis ◽  
Anne Ngu ◽  
Coralys Rivera
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Naive Bayes ◽  
Fall Detection ◽  
Learning Model ◽  
Naïve Bayes ◽  
Sensor Data ◽  
Support Vector ◽  
Accelerometer Data ◽  
Deep Learning Model

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.

scholarly journals Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Daohua Pan ◽  
Hongwei Liu ◽  
Dongming Qu ◽  
Zhan Zhang
Keyword(s):  
Data Fusion ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Support Vector ◽  
Training Set ◽  
Multisensor Data Fusion ◽  
Intelligent Devices ◽  
Multisensor Data

Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.

The MobiFall Dataset

2014 ◽  
Vol 2 (1) ◽  
pp. 44-56 ◽  
Author(s):  
George Vavoulas ◽  
Matthew Pediaditis ◽  
Charikleia Chatzaki ◽  
Emmanouil G. Spanakis ◽  
Manolis Tsiknakis
Keyword(s):  
Assisted Living ◽  
Inertial Sensor ◽  
Fall Detection ◽  
The Elderly ◽  
Research Literature ◽  
Sensor Data ◽  
Detection Methods ◽  
Communication Services ◽  
Detection Systems ◽  
Primary Signal

Fall detection is receiving significant attention in the field of preventive medicine, wellness management and assisted living, especially for the elderly. As a result, several fall detection systems are reported in the research literature or exist as commercial systems. Most of them use accelerometers and/ or gyroscopes attached on a person's body as the primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency when necessary. Nevertheless, automatic fall detection continues to present significant challenges, with the recognition of the type of fall being the most critical. The aim of this work is to introduce a human fall and activity dataset to be used in testing new detection methods, as well as performing objective comparisons between different reported algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different types of falls and nine different activities of daily living. Utilizing this dataset, the results of an elaborate evaluation of machine learning-based fall detection and fall classification are presented and discussed in detail.

Design and Implementation of Fall Detection System Based on ZigBee

2013 ◽  
Vol 397-400 ◽  
pp. 1446-1450
Author(s):  
Yi Zhang ◽  
Gang Tan ◽  
Yuan Luo ◽  
Yang Li
Keyword(s):  
Mobile Phones ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Design And Implementation ◽  
Weighted Fusion ◽  
Detection Technology ◽  
Alert Message

Elderly people are unable to be rescued promptly when they fall down from the chair or bed, and may be injured seriously. A fall detection system for chair based on ZigBee was investigated in this paper for this problem. Gotten the fallen information by multi-sensor data acquisition and detection technology, uploaded to PC or Android terminals through ZigBee-WiFi gateway, and sent the information to mobile phone in SMS through GSM module. The adaptive weighted fusion algorithm was used to improve the accuracy of monitoring-data. The results show that one who take the system can get the alert message instantly by PC, Android terminals and mobile phones, so it has the potential to satisfy the application of the elderly peoples care problem.

scholarly journals Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

2021 ◽  
pp. 158-166
Author(s):  
Noah Balestra ◽  
Gaurav Sharma ◽  
Linda M. Riek ◽  
Ania Busza
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Sensor Data ◽  
Inpatient Setting ◽  
Accelerometer Data ◽  
Data Set ◽  
Machine Learning Classification ◽  
Exercise Type ◽  
Exercise Dose ◽  
Rehabilitation Exercises

<b><i>Background:</i></b> Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. <b><i>Objectives:</i></b> The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. <b><i>Methods:</i></b> MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (<i>n</i> = 13) and individuals with upper extremity weakness due to recent stroke (<i>n</i> = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. <b><i>Results:</i></b> We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. <b><i>Conclusions:</i></b> Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

scholarly journals AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1889
Author(s):  
Francisco Luna-Perejón ◽  
Luis Muñoz-Saavedra ◽  
Javier Civit-Masot ◽  
Anton Civit ◽  
Manuel Domínguez-Morales
Keyword(s):  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Life Activities ◽  
Learning Classifier ◽  
The People ◽  
Activity Types ◽  
Remote Monitoring Systems ◽  
Secondary Study

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.

scholarly journals Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Melinda G. Conners ◽  
Théo Michelot ◽  
Eleanor I. Heywood ◽  
Rachael A. Orben ◽  
Richard A. Phillips ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Flapping Flight ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Fine Scale ◽  
Magnetometer Data ◽  
Behavioral Classification ◽  
Soaring Flight

AbstractBackgroundInertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective  classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.MethodsWe deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.ResultsHMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.ConclusionsThe use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

scholarly journals A Survey on Classification algorithms of Brain Images in Alzheimer’s disease based on Feature Extraction techniques

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ruhul Amin Hazarika ◽  
Arnab Kumar Maji ◽  
Samarendra Nath Sur ◽  
Babu Sena Paul ◽  
Debdatta Kandar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Classification Algorithms ◽  
Extraction Techniques ◽  
Brain Images

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

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