CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Sensor-Based Human Activity Recognition <s> Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network <s> BIB001 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Sensor-Based Human Activity Recognition <s> There are various applications of physical activity monitoring for medical purposes, such as therapeutic rehabilitation, fitness enhancement or the use of physical activity as context information for evaluation of other vital data. Physical activity can be estimated using acceleration sensor-systems fixed on a person's body. By means of pattern recognition methods, it is possible to identify with certain accuracy which movement is being performed. This work presents a comparison of different methods for recognition of daily-life activities, which will serve as basis for the development of an online activity monitoring system. <s> BIB002 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Sensor-Based Human Activity Recognition <s> The Activity Monitor described in this paper is an easy-to-configure context-aware mobile application, capable of estimating and evaluating the user's activity all day long. It relies on fusion strategies for movement and location estimation, which combine acceleration and radio data from in-device and external sensors. The final objective of the Activity Monitor is to deliver adequate context-aware notifications in order to make the user aware of his level of activity. The reasoning process to decide when and how to deliver notifications is fully done in the mobile device by using an embedded reasoner, avoiding privacy issues related to personal sensitive data sharing. <s> BIB003 </s> CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies <s> Sensor-Based Human Activity Recognition <s> Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks. <s> BIB004
Sensor-based human activity recognition is to directly attach some sensors on human's body for activity recognition. Wearable sensors, such as, accelerometer, gyroscope, thermometer and heart monitor, are attached to the body to obtain the information of human motion BIB003 , temperature BIB001 and health condition BIB002 , etc. Some sensors have been encapsulated into the mobile phone or smartwatches, so people can just use those devices for activity recognition, which can be quite convenient. The general workflow of sensor-based human activity recognition is as follows: (1) Data Collection; (2) Data Preprocessing; (3) Feature Extraction; and (4) Activity Recognition. Sensors and attributes are selected to collect data during data collection. Since there are some noises or redundancy in the raw data of the collected data, we need to calibrate the sensor data for further use. Concerning the step of feature extraction, many works tend to extract the features defined by themselves in which features can be extracted and selected one by one statistically and analytically, while people are now using the deep learning algorithm to learn the features automatically without giving too much interpretation or explanation of the properties of the extracted features BIB004 . However, there is a trade-off between the recognition performance and computation resources when applied different learning algorithms on activity recognition. For example, if mobile phones are used for activity recognition by applying deep learning algorithm, since the computation costs a lot, although the result is decent, the battery will soon run out.