Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step

Increase in workload across many organisations and consequent increase in occupational stress is negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of self- reporting and variability between and within individuals. With the advent of smartphones it is now possible to monitor diverse aspects of human behaviour, including objectively measured behaviour related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behaviour that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (in comparison to location, video or audio recording, for example) and because its low power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. 30 subjects from two different organizations were provided with smartphones. The study lasted for 8 weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify self reported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.Comment: in IEEE Journal of Biomedical and Health Informatics, 201

Long-term Activities Segmentation using Viterbi Algorithm with a k-minimum-consecutive-states Constraint

AbstractIn the last years, several works have made use of acceleration sensors to recognize simple physical activities like: walking, running, sleeping, falling, etc. Many of them rely on segmenting the data into fixed time windows and computing time domain and/or frequency domain features to train a classifier. A long-term activity is composed of a collection of simple activities and may last from a few minutes to several hours (e.g., shopping, exercising, working, etc.). Since long-term activities are more complex and their duration varies greatly, generating fixed length segments is not suitable. For this type of activities the segmentation should be done dynamically. In this work we propose the use of the Viterbi algorithm on a Hidden Markov Model with the addition of a k-minimum-consecutive-states constraint to perform the long-term activity recognition and segmentation from accelerometer data. This constraint allows the algorithm to perform a more informed search by incorporating prior knowledge about the minimum duration of each long-term activity. Our experiments showed good results for the activity recognition task and it was demonstrated that the accuracy was significantly increased by adding the k-minimum-consecutive-states constraint

Designing a Modeling Language for Customer Journeys: Lessons Learned from User Involvement

Although numerous methods have been formalized for handling the technical aspects of developing domain-specific modeling languages (DSMLs), user needs and usability aspects are often addressed in ad hoc manners and late in the development process. Working in this context, this paper presents the development of the customer journey modeling language (CJML), a DSML for modeling service processes from the end-user's perspective. CJML targets a wide and heterogeneous group of users, making it especially challenging regarding usability. This paper describes how an industry-relevant DSML was systematically improved by using a variety of user-centered design techniques in close collaboration with the target group and how their feedback was used to refine and evolve the syntax and semantics of CJML. We also suggest how a service-providing organization may benefit from adopting CJML as a unifying language for documentation purposes, compliance analysis, and service innovation. Finally, we generalize the experience gained into lessons learned and methodological guidelines.acceptedVersio

User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation

Kan bare brukes i forskningssammenheng, ikke kommersielt. Les mer her: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsBuilding predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users’ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models’ performance.acceptedVersio

A Systematic Mapping Study on Approaches for AI-Supported Security Risk Assessment

Effective assessment of cyber risks in the increasingly dynamic threat landscape must be supported by artificial intelligence techniques due to their ability to dynamically scale and adapt. This article provides the state of the art of AI-supported security risk assessment approaches in terms of a systematic mapping study. The overall goal is to obtain an overview of security risk assessment approaches that use AI techniques to identify, estimate, and/or evaluate cyber risks. We carried out the systematic mapping study following standard processes and identified in total 33 relevant primary studies that we included in our mapping study. The results of our study show that on average, the number of papers about AI-supported security risk assessment has been increasing since 2010 with the growth rate of 133% between 2010 and 2020. The risk assessment approaches reported have mainly been used to assess cyber risks related to intrusion detection, malware detection, and industrial systems. The approaches focus mostly on identifying and/or estimating security risks, and primarily make use of Bayesian networks and neural networks as supporting AI methods/techniques.acceptedVersio

Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review

Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the process and product quality. Many frameworks for implementing CPS have been developed to structure the relationship between the digital and the physical worlds. However, there is no systematic review of the existing frameworks related to quality management in manufacturing CPS. Thus, our study aims at determining and comparing the existing frameworks. The systematic review yielded 38 frameworks analyzed regarding their characteristics, use of data science and Machine Learning (ML), and shortcomings and open research issues. The identified issues mainly relate to limitations in cross-industry/cross-process applicability, the use of ML, big data handling, and data security.publishedVersio

On The Reliability Of Machine Learning Applications In Manufacturing Environments

The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the productive use of machine learning (ML) algorithms in the manufacturing domain. As ML applications transcend from research to productive use in real-world industrial environments, the question of reliability arises. Since the majority of ML models are trained and evaluated on static datasets, continuous online monitoring of their performance is required to build reliable systems. Furthermore, concept and sensor drift can lead to degrading accuracy of the algorithm over time, thus compromising safety, acceptance and economics if undetected and not properly addressed. In this work, we exemplarily highlight the severity of the issue on a publicly available industrial dataset which was recorded over the course of 36 months and explain possible sources of drift. We assess the robustness of ML algorithms commonly used in manufacturing and show, that the accuracy strongly declines with increasing drift for all tested algorithms. We further investigate how uncertainty estimation may be leveraged for online performance estimation as well as drift detection as a first step towards continually learning applications. The results indicate, that ensemble algorithms like random forests show the least decay of confidence calibration under drift.publishedVersio

HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features

In this paper, we present HTAD: A Home Tasks Activities Dataset. The dataset contains wrist-accelerometer and audio data from people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such as elderly care and mental health monitoring. Preliminary results show that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this dataset public, researchers can test different machine learning algorithms for activity recognition, especially, sensor data fusion methodsacceptedVersio