Revisiting “Recognizing Human Activities User- Independently on Smartphones Based on Accelerometer Data” – What Has Happened Since 2012?

Our article “Recognizing human activities user-independently on smartphones based on accelerometer data” was published in the International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) in 2012. In 2018, it was selected as the most outstanding article published in the 10 years of IJIMAI life. To celebrate the 10th anniversary of IJIMAI, in this article we will introduce what has happened in the field of human activity recognition and wearable sensor-based recognition since 2012, and especially, this article concentrates on introducing our work since 2012

Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition

This article studies how to detect and explain concept drift. Human activity recognition is used as a case study together with a online batch learning situation where the quality of the labels used in the model updating process starts to decrease. Drift detection is based on identifying a set of features having the largest relevance difference between the drifting model and a model that is known to be accurate and monitoring how the relevance of these features changes over time. As a main result of this article, it is shown that feature relevance analysis cannot only be used to detect the concept drift but also to explain the reason for the drift when a limited number of typical reasons for the concept drift are predefined. To explain the reason for the concept drift, it is studied how these predefined reasons effect to feature relevance. In fact, it is shown that each of these has an unique effect to features relevance and these can be used to explain the reason for concept drift.Comment: Accepted to HASCA 2022 workshop in conjunction with UbiComp/ISWC202

Photoplethysmographic measurements of arterial and aortic pulse waveform characteristics

The photoplethysmographic (PPG) signal is a complex signal, composed of the peripheral pulse synchronized to each heartbeat (the fluctuating component), and modulated by a slow component that varies due to respiration, vasomotor activity and vasoconstrictor waves, ECG and pulse waves from healthy subjects. Decomposition of the PPG pulse waves produces five components: percussion, tidal, dicrotic, repercussion, and retidal waves. Pulse wave decomposition parameters PPG are compared to detect variables for information on person’s arterial elasticity. Nowadays, promising cardiovascular parameters registration method is PPG, which is relatively simple to be applied in eHealth, clinical applications, homecare, drives’ sleepiness, or even endothelial dysfunction. Results show that elasticity information in the form of pulse wave decomposition from PPG waves is easily obtainable and shows clear shortening between percussion wave and tidal wave peak time in PPG waveforms as a function of age. Decomposition analysis is valuable in assessment of the arterial elasticity. In addition, PPG measurement is absolutely non‐invasive and safe. In PPG measurement, the sensors are on the opposite sides of the fingertip to obtain accurate waveforms. A further challenge is the calibration of the PPG measurement systems in order to achieve comparative diagnostic relations, because PPG waveforms in different regions of the body and in different subjects do not allow us to find a universal calibration function for reliable estimations of the clinical data

Recognizing Human Activities Userindependently on Smartphones Based on Accelerometer Data

Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent

Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry

Software applications used in the controlling and planning of production processes commonly make use of predictive statistical models. Changes in the process involve a more or less regular need for updating the prediction models on which the operational software applications are based. The objective of this article is • to provide information which helps to design semiautomatic systems for the maintenance of statistical prediction models and • to describe a proof-of-concept implementation in an industrial application. The system developed processes the production data and provides an easy-to-use interface to construct updated models and introduce them into a software application. The article presents the architecture of the maintenance system, with a description of the algorithms that cause the system’s functionality. The system developed was implemented for keeping up-to-date prediction models which are in everyday use in a steel plate mill in the planning of the mechanical properties of steel products. The conclusion of the results is that the semi-automatic approach proposed is competitive with fully automatic and manual approaches. The benefits include good prediction accuracy and decreased workload of the deployment of updated model versions

Using a Semi-autonomous Drone Swarm to Support Wildfire Management – A Concept of Operations Development Study

This paper provides insights into a human factors-oriented Concept of Operations (ConOps), which can be applied for future semi-autonomous drone swarms to support the management of wildfires. The results provide, firstly, an overview of the current practices to manage wildfires in Finland. Secondly, some of the current challenges and future visions about drone usage in a wildfire situation are presented. Third, a description of the key elements of the developed future ConOps for operating a drone swarm to support the combat of wildfires is given. The ConOps has been formulated based on qualitative research, which included a literature review, seven subject matter expert interviews and a workshop with 40 professionals in the domain. Many elements of this ConOps may also be applied to a variety of other swarm robotics operations than only wildfire management. Finally, as the development of the ConOps is still in its first stage, several further avenues for research and development are proposed

A distributed multi-robot sensing system using an infrared location system

Abstract-Distributed sensing refers to measuring systems where instead of one sensor multiple sensors are spatially distributed improving robustness of the system, increasing relevancy of the measurements and cutting costs since requiring smaller and less precise sensors. Spatially distributed sensors fuse their measurements into the same coordinates requiring relative positions of the sensors. In this paper, we present a distributed multi-robot sensing system in which relative poses (positions and orientations) among robots are estimated using an infrared location system. The relative positions are estimated using intensity and bearing measurements of the received infrared signals. The relative orientations are obtained by fusing position estimates among robots. The location system enables a group of robots to perform distributed and cooperative environment sensing by maintaining a given formation while the group measures distributions of light and magnetic field, for example. In the experiments, a group of three robots moves and collects spatial information (i.e. illuminance and compass heading) from the given environment. The information is stored into grid maps and illustrated in the figures presenting illuminance and compass heading. The experiments proved the feasibility of the distributed multi-robot sensing system for sensing applications where the environment requires moving platforms