Comparing CNN and Human Crafted Features for Human Activity Recognition

Deep learning techniques such as Convolutional Neural Networks (CNNs) have shown good results in activity recognition. One of the advantages of using these methods resides in their ability to generate features automatically. This ability greatly simplifies the task of feature extraction that usually requires domain specific knowledge, especially when using big data where data driven approaches can lead to anti-patterns. Despite the advantage of this approach, very little work has been undertaken on analyzing the quality of extracted features, and more specifically on how model architecture and parameters affect the ability of those features to separate activity classes in the final feature space. This work focuses on identifying the optimal parameters for recognition of simple activities applying this approach on both signals from inertial and audio sensors. The paper provides the following contributions: (i) a comparison of automatically extracted CNN features with gold standard Human Crafted Features (HCF) is given, (ii) a comprehensive analysis on how architecture and model parameters affect separation of target classes in the feature space. Results are evaluated using publicly available datasets. In particular, we achieved a 93.38% F-Score on the UCI-HAR dataset, using 1D CNNs with 3 convolutional layers and 32 kernel size, and a 90.5% F-Score on the DCASE 2017 development dataset, simplified for three classes (indoor, outdoor and vehicle), using 2D CNNs with 2 convolutional layers and a 2x2 kernel size

Is identifying boredom the answer to controlling the bombardment of notifications on mobile devices?

Mobile notifications have become ubiquitous in modern life, yet excessive volumes contribute to interruption overload. This paper investigates intelligent notification management leveraging user context. A three-stage methodology employed a focus group, survey, and in-the-wild data collection app. The focus group (n=12) provided preliminary insights into notification perceptions during boredom which informed survey design. The survey (n=106) probed usage habits across times, days, and app categories. The SeektheNotification app gathered real-world notification data from 20 Android users over 3 months.Analysis revealed social and personal apps dominate notification volumes (91% combined). Shorter response delays occurred on weekends and after 12 pm, suggesting heightened user receptivity during boredom. Random Forest classification achieved 88% accuracy, outperforming 13 other algorithms, underscoring machine learning’s potential for context-aware notification systems.Our exploratory findings indicate notifications could be optimized by considering situational factors like boredom. Further research should expand context beyond boredom and employ advanced deep learning techniques. This preliminary study demonstrates the promise of leveraging user psychology and machine intelligence to develop smarter interruption management systems to combat notification overload

Mobile agent path planning under uncertain environment using reinforcement learning and probabilistic model checking

The major challenge in mobile agent path planning, within an uncertain environment, is effectively determining an optimal control model to discover the target location as quickly as possible and evaluating the control system's reliability. To address this challenge, we introduce a learning-verification integrated mobile agent path planning method to achieve both the effectiveness and the reliability. More specifically, we first propose a modified Q-learning algorithm (a popular reinforcement learning algorithm), called Q EA−learning algorithm, to find the best Q-table in the environment. We then determine the location transition probability matrix, and establish a probability model using the assumption that the agent selects a location with a higher Q-value. Secondly, the learnt behaviour of the mobile agent based on Q EA−learning algorithm, is formalized as a Discrete-time Markov Chain (DTMC) model. Thirdly, the required reliability requirements of the mobile agent control system are specified using Probabilistic Computation Tree Logic (PCTL). In addition, the DTMC model and the specified properties are taken as the input of the Probabilistic Model Checker PRISM for automatic verification. This is preformed to evaluate and verify the control system's reliability. Finally, a case study of a mobile agent walking in a grids map is used to illustrate the proposed learning algorithm. Here we have a special focus on the modelling approach demonstrating how PRISM can be used to analyse and evaluate the reliability of the mobile agent control system learnt via the proposed algorithm. The results show that the path identified using the proposed integrated method yields the largest expected reward.</p

Acceptability of a Mobile App (iMPAKT) for Measurement and Implementation of Person-Centredness: Mixed-Methods Study

This study tested the acceptability of the iMPAKT App with end users. Cognitive task analysis and semi-structured interviews were used. Twelve participants took part. The majority of tasks were found to be easy to complete but issues were identified with a section of the app that provides Speech-To-Text transcription of patients speaking about their experience of care. Artificial Intelligence (AI) based systems may be needed to address these limitations. Overall views on acceptability of the app were positive and participants valued how it could be used to support practice improvement initiatives and large scale collection of person-centred measures

Determination of the most relevant features to improve the performance of RF classifier in human activity recognition

The impact that neurodegenerative diseases have in our society, have made human activity recognition (HAR) arise as a relevant field of study. The quality of life of people with such conditions, can be significantly improved with the outcomes of the projects within this area. The application of machine learning techniques on data from low level sensors such as accelerometers is the base of HAR. To improve the performance of these classifiers, it is necessary to carry out an adequate training process. To improve the training process, an analysis of the different features used in literature to tackle these problems was performed on datasets constructed with students performing 18 different activities of daily living. The outcome of the process shows that an adequate selection of features improves the performance of Random Forest from 94.6% to 97.2%. It was also found that 78 features explain 80% of the variability