scholarly journals Motion Type Verification Studies Using Accelerometer Sensor Data With Local Mean Decomposition

2019 ◽  
Vol 2 (3) ◽  
pp. 1051-1057
Author(s):  
Mustafa Yasin Esas ◽  
Fatma Latifoğlu
Keyword(s):  
Human Physiology ◽  
Cell Phone ◽  
High Accuracy ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Accelerometer Sensor ◽  
Local Mean Decomposition ◽  
A Cell ◽  
Local Mean ◽  
High Degree

It is a significant improvement that the physical movements directly related to human physiology can be detected with high accuracy using sensors. In our study, three-axis accelerometer data recorded using a cell phone sensor in a controlled manner were used. Validation of walking, jogging, up-stairs, down-stair movements is aimed. For this purpose, local mean decomposition (LMD) function was used. The axis (x, y, z) in which the orthogonality value obtained from LMD was high was determined. Then, it was evaluated that there is movement in the direction of high value axis. While there is a high degree of accuracy in up-stair, down-stair and jogging movements, the desired success in walking movement was not achieved.

Anomaly Detection in Streaming Sensor Data

2010 ◽  
pp. 99-117 ◽  
Author(s):  
Alec Pawling ◽  
Ping Yan ◽  
Julián Candia ◽  
Tim Schoenharl ◽  
Greg Madey
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Anomaly Detection ◽  
Emergency Response ◽  
Support System ◽  
Cell Phone ◽  
Scientific Work ◽  
Streaming Data ◽  
Sensor Data ◽  
A Cell

This chapter considers a cell phone network as a set of automatically deployed sensors that records movement and interaction patterns of the population. The authors discuss methods for detecting anomalies in the streaming data produced by the cell phone network. The authors motivate this discussion by describing the Wireless Phone Based Emergency Response (WIPER) system, a proof-of-concept decision support system for emergency response managers. This chapter also discusses some of the scientific work enabled by this type of sensor data and the related privacy issues. The authors describe scientific studies that use the cell phone data set and steps we have taken to ensure the security of the data. The authors also describe the overall decision support system and discuss three methods of anomaly detection that they have applied to the data.

scholarly journals Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 174
Author(s):  
Junhyuk Kang ◽  
Jieun Shin ◽  
Jaewon Shin ◽  
Daeho Lee ◽  
Ahyoung Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Signal ◽  
Short Term Memory ◽  
Learning Model ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Accelerometer Sensor ◽  
Skeleton Image ◽  
Deep Learning Model

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

Anomaly Detection in Streaming Sensor Data

2012 ◽  
pp. 910-928
Author(s):  
Alec Pawling ◽  
Ping Yan ◽  
Julián Candia ◽  
Tim Schoenharl ◽  
Greg Madey
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Anomaly Detection ◽  
Emergency Response ◽  
Support System ◽  
Cell Phone ◽  
Scientific Work ◽  
Streaming Data ◽  
Sensor Data ◽  
A Cell

This chapter considers a cell phone network as a set of automatically deployed sensors that records movement and interaction patterns of the population. The authors discuss methods for detecting anomalies in the streaming data produced by the cell phone network. The authors motivate this discussion by describing the Wireless Phone Based Emergency Response (WIPER) system, a proof-of-concept decision support system for emergency response managers. This chapter also discusses some of the scientific work enabled by this type of sensor data and the related privacy issues. The authors describe scientific studies that use the cell phone data set and steps we have taken to ensure the security of the data. The authors also describe the overall decision support system and discuss three methods of anomaly detection that they have applied to the data.

scholarly journals Human Movement Monitoring and Analysis for Prehabilitation Process Management

2020 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Khalid Al-Naime ◽  
Adnan Al-Anbuky ◽  
Grant Mawston
Keyword(s):  
Fourier Analysis ◽  
High Intensity ◽  
Hospital Setting ◽  
Human Movement ◽  
Dominant Frequency ◽  
Sensor Data ◽  
Moderate Intensity ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Accelerometer Sensor

Cancer patients assigned for abdominal surgery are often given exercise programmes (prehabilitation) prior to surgery, which aim to improve fitness in order to reduce pre-operative risk. However, only a small proportion of patients are able to partake in supervised hospital-based prehabilitation because of inaccessibility and a lack of resources, which often makes it difficult for health professionals to accurately monitor and provide feedback on exercise and activity levels. The development of a simple tool to detect the type and intensity of physical activity undertaken outside the hospital setting would be beneficial to both patients and clinicians. This paper aims to describe the key exercises of a prehabilitation programme and to determine whether the types and intensity of various prehabilitation exercises could be accurately identified using Fourier analysis of 3D accelerometer sensor data. A wearable sensor with an inbuilt 3D accelerometer was placed on both the ankle and wrist of five volunteer participants during nine prehabilitation exercises which were performed at low to high intensity. Here, the 3D accelerometer data are analysed using fast Fourier analysis, where the dominant frequency and amplitude components are extracted for each activity performed at low, moderate, and high intensity. The findings indicate that the 3D accelerometer located at the ankle is suitable for detecting activities such as cycling and rowing at low, moderate, and high exercise intensities. However, there is some overlap in the frequency and acceleration amplitude components for overland and treadmill walking at a moderate intensity.

scholarly journals What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6816
Author(s):  
Seer J. Ikurior ◽  
Nelly Marquetoux ◽  
Stephan T. Leu ◽  
Rene A. Corner-Thomas ◽  
Ian Scott ◽  
...  
Keyword(s):  
Animal Health ◽  
Sensor Data ◽  
Diel Activity ◽  
Parasitic Nematodes ◽  
Accelerometer Data ◽  
Triaxial Accelerometer ◽  
Activity Budgets ◽  
Accelerometer Sensor ◽  
Ewe Lambs ◽  
The Impact

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.

The Evolution of Augmentative Communication and the Importance of Alternate Access

2011 ◽  
Vol 20 (1) ◽  
pp. 34-37 ◽  
Author(s):  
David Chapple
Keyword(s):  
Augmentative And Alternative Communication ◽  
Cell Phone ◽  
Handheld Devices ◽  
Alternative Communication ◽  
Access Methods ◽  
The Past ◽  
Communication Device ◽  
Internet Browser ◽  
A Cell ◽  
Development Of Language

Abstract Over the past 20 years, there have been many advances in the computer industry as well as in augmentative and alternative communication (AAC) devices. Computers are becoming more compact and have multiple purposes, such as the iPhone, which is a cell phone, mp3 player, and an Internet browser. AAC devices also have evolved to become multi-purpose devices; the most sophisticated devices have functionality similar to the iPhone and iPod. Recently, the idea of having the iPhone and iPad as a communication device was initiated with the development of language applications specifically for this format. It might be true that this idea could become the future of AAC devices; however, there are major access issues to overcome before the idea is a reality. This article will chronicle advancements in AAC devices, specifically on access methods, throughout the years, towards the transition to handheld devices. The newest technologies hold much promise with both features and affordability factors being highly attractive. Yet, these technologies must be made to incorporate alternate access if they are to meet their fullest potential as AAC tools.

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.

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