Signal processing technologies for activity-aware smart textiles

Roggen, Daniel; Tröster, Gerhard; Bulling, Andreas

doi:10.1533/9780857093530.2.329

Cited by 6 publications

(5 citation statements)

References 119 publications

(108 reference statements)

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“…Popular methodologies for state estimation of a complex system include the: Maximum Likelihood estimation (ML) [ 20 ], Kalman filters [ 21 ], particle filters [ 22 ], covariance intersection and covariance union techniques [ 23 ], etc. The term Decision fusion mainly refers to the combination of different decisions deriving from different classifiers into a common decision regarding an activity that has occurred [ 24 ]. Some examples of decision fusion include Bayesian inference [ 25 ], Dempster Shafer Inference [ 26 ], abductive reasoning utilizing neural networks [ 27 ] or fuzzy logic [ 28 ], utilization of semantic features for decision making [ 29 ], etc.…”

Section: Data Fusion Techniques In Literature Categorizationmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

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Section: Data Fusion Techniques In Literature Categorizationmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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“…Human physical activity refers to static (sitting, standing, lying) postures. [50] [51], transition activities (sit-to-stand, sit-to lie, stand to-walk) [52], [53], Dynamic motions (Walking, Running, stairs climbing, exercising, housed hold chores) [54] [55]. In previous research, Human Activity Recognition (HAR) uses different types of approaches, such as: 1) Computer vision-based HAR-It uses cameras to record the various activities [56] [53] [57] 2) Environmental sensor-based HAR-In order to detect events, sensors and signals such as sound sensors, light sensors or RFID tags are used, [58] [59] 3) Wearable sensorbased HAR-These are the sensors that are mounted on the various parts of the body, such as accelerometers, strain, stretch, and then analyze the information to identify the activities [21] [14], [60], [61] and 4) Time geography-based HAR-Using time and location data to classify human activities [62] [53], [63]- [65].…”

Section: Literature Reviewmentioning

confidence: 99%

“…There is a change in sensor parameters when there is a sudden pressing or twisting of a subject's elbow or hand. The respondent was chosen for the collection of data by age (20)(21)(22)(23)(24)(25) and weight (45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55) kg. The data is collected at a frequency of 50 milliseconds.…”

Section: A) Data Collectionmentioning

confidence: 99%

Identification of Violent Response using Feature Extraction Matrix Algorithm of a Time Series Data (FEM)

Randhawa

Shanthagiri²,

Alharbi

et al. 2022

Preprint

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There are several women safety devices in the market today. Women who suffer these atrocities are even denied basic human rights, as set out in the Criminal Code. Women who are not as fit (physically) as men need to be protected from the evils of society. The introduction of actions and procedures for healthier women is not adequate and needs to be well improved. However, these devices are not fool- proof. The crux of this paper is a partial result of a challenging problem faced during design and construction a fool-proof Smart Jacket for women’s safety using fabric sensors. The smart jacket is envisioned for the women to wear on all occasions. An alert message and subjects’ Geo-location is sent to pre-assigned phone number if the subject is faced with violent situation. The jacket consists of fabric Sensors, Accelerometer, Gyroscope and Magnetometer, which are strategically placed to record maximum variations in signal for minimum movement in subject’s body. The primary challenge is not in design or construction of a jacket, but in accurately classifying violent activity from animated activity. Both violent activity and animated activities have commonality in sensor excitation. There are subtle differences which needs to be extracted to train a Machine learning algorithm to learn these patterns. Multivariate Regression Analysis (MRA) is used to highlight explanatory variables that can produce better convergence. With MRA results signifying a high degree of noise in the data, a newer approach, wherein, the ordinality of the data was ignored and cardinality of the data was considered for analysis. Machine learning approaches is then used to build a model using features extracted through MRA. Different machine learning techniques have been applied to classify the violent attack from the different activities such as stationary subject, walking, dancing. This study present that Support vector machine shows the better classification accuracy, computation time as compared to other algorithms.

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“…However, if it has an additional camera or LiDAR, it can navigate itself to a safe place after successfully avoiding the obstacle, if such logic is built-in for that failure. Roggen et al, Luo et al, and Foo et al [41][42][43] performed a study on high-level decision data fusion and concluded that using multiple sensors with data fusion is better than individual sensors without data fusion. In addition to the above, several researchers [27,39,[44][45][46] discovered that every sensor used provides a different type, sometimes unique type of information in the selected environment, which includes the tracked object, avoided object, the autonomous vehicle itself, the world it is being used, and so on and so forth, and the information is provided with differing accuracy and differing details.…”

Section: Multiple Sensors Vs Single Sensormentioning

confidence: 99%

“…Decision or High-level data fusion. At the highest level, the system decides the major tasks and takes decisions based on the fusion of information, which is input from the system features [41,43]. 2.…”

Section: Levels Of Data Fusion Applicationmentioning

confidence: 99%

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Kolar

Benavidez

Jamshidi

2020

Sensors

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This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, senior citizens, and so on and are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for fusion of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement data fusion using different sensors like LiDAR that use light scan technology, stereo/depth cameras, Red Green Blue monocular (RGB) and Time-of-flight (TOF) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation.

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Signal processing technologies for activity-aware smart textiles

Cited by 6 publications

References 119 publications

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Identification of Violent Response using Feature Extraction Matrix Algorithm of a Time Series Data (FEM)

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

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