scholarly journals Sensor Fusion for Recognition of Activities of Daily Living

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4029 ◽  
Author(s):  
Jiaxuan Wu ◽  
Yunfei Feng ◽  
Peng Sun
Keyword(s):  
Time Series ◽  
Sensor Fusion ◽  
Daily Living ◽  
Single Point ◽  
Recognition System ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Life Patterns ◽  
Functional Capabilities ◽  
Correction Technique

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 217 ◽  
Author(s):  
Alaa E. Abdel Hakim ◽  
Wael Deabes
Keyword(s):  
Performance Improvement ◽  
Daily Living ◽  
Input Data ◽  
Recognition System ◽  
Learning System ◽  
Sensor Data ◽  
Hybrid Combination ◽  
Single Instance ◽  
Recognition Systems ◽  
Human Annotator

In supervised Activities of Daily Living (ADL) recognition systems, annotating collected sensor readings is an essential, yet exhaustive, task. Readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the produced dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single “Unknown” or “Do-Nothing” label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every set of them a unique label identifying the encapsulating certain labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than 2.5 × 10 6 sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.

Sensor Fusion Algorithm that Has Resilience under Autonomous Driving Conditions: A Survey

2017 ◽  
Vol 865 ◽  
pp. 429-433
Author(s):  
Sung Bum Park ◽  
Hyeok Chan Kwon ◽  
Dong Hoon Lee
Keyword(s):  
Sensor Fusion ◽  
Autonomous Driving ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Multiple Sensors ◽  
Autonomous Cars ◽  
Driving Conditions

Autonomous cars recognize the surroundings through multiple sensors and make decisions to control the car in order to arrive at destination without driver's interventions. In such environment, if sensor data forgery occurs, it could lead to a big (critical) accident that could threaten the life of the driver. In the paper, a research on a way to get accurate driving information through sensor fusion algorithm that has resilience against data forgery and modulation will be discussed.

scholarly journals Multi-Sensor Fusion for Activity Recognition—A Survey

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3808 ◽  
Author(s):  
Antonio A. Aguileta ◽  
Ramon F. Brena ◽  
Oscar Mayora ◽  
Erik Molino-Minero-Re ◽  
Luis A. Trejo
Keyword(s):  
Sensor Fusion ◽  
Activity Recognition ◽  
Sports Medicine ◽  
Good Practice ◽  
Sensor Data ◽  
Personal Safety ◽  
Multiple Sensors ◽  
Concurrent Use ◽  
Fusion Methods ◽  
The Many

In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.

scholarly journals Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Muchun Su ◽  
Diana Wahyu Hayati ◽  
Shaowu Tseng ◽  
Jiehhaur Chen ◽  
Hsihsien Wei
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Activities Of Daily Living ◽  
Video Processing ◽  
Daily Living ◽  
Deep Neural Network ◽  
Recognition System ◽  
Automatic Recognition ◽  
Data Filtering ◽  
Accuracy Rate

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

Forecasting Techniques for Time Series from Sensor Data

2015 ◽  
Author(s):  
Adriana Horelu ◽  
Catalin Leordeanu ◽  
Elena Apostol ◽  
Dan Huru ◽  
Mariana Mocanu ◽  
...  
Keyword(s):  
Time Series ◽  
Sensor Data ◽  
Forecasting Techniques

Low Cost Sensor Data Fusion in Omnidirectional Mobile Robot Feedback System to Improve the Navigation Accuracy

2014 ◽  
Vol 607 ◽  
pp. 791-794 ◽  
Author(s):  
Wei Kang Tey ◽  
Che Fai Yeong ◽  
Yip Loon Seow ◽  
Eileen Lee Ming Su ◽  
Swee Ho Tang
Keyword(s):  
Mobile Robot ◽  
Sensor Fusion ◽  
Low Cost ◽  
Feedback System ◽  
Sensor Data ◽  
Research Setting ◽  
Fusion Technique ◽  
Omnidirectional Mobile Robot ◽  
The Cost ◽  
Navigation Accuracy

Omnidirectional mobile robot has gained popularity among researchers. However, omnidirectional mobile robot is rarely been applied in industry field especially in the factory which is relatively more dynamic than normal research setting condition. Hence, it is very important to have a stable yet reliable feedback system to allow a more efficient and better performance controller on the robot. In order to ensure the reliability of the robot, many of the researchers use high cost solution in the feedback of the robot. For example, there are researchers use global camera as feedback. This solution has increases the cost of the robot setup fee to a relatively high amount. The setup system is also hard to modify and lack of flexibility. In this paper, a novel sensor fusion technique is proposed and the result is discussed.

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