A wearable and non-wearable approach for gesture recognition: initial results

A natural way of communication between humans are gestures. Through this type of non-verbal communication, the human interaction may change since it is possible to send a particular message or capture the attention of the other peer. In the human-computer interaction the capture of such gestures has been a topic of interest where the goal is to classify human gestures in different scenarios. Applying machine learning techniques, one may be able to track and recognize human gestures and use the gathered information to assess the medical condition of a person regarding, for example, motor impairments. According to the type of movement and to the target population one may use different wearable or non-wearable sensors. In this work, we are using a hybrid approach for automatically detecting the ball throwing movement by applying a Microsoft Kinect (non-wearable) and the Pandlet (set of wearable sensors such as accelerometer, gyroscope, among others). After creating a dataset of 10 participants, a SVM model with a DTW kernel is trained and used as a classification tool. The system performance was quantified in terms of confusion matrix, accuracy, sensitivity and specificity, Area Under the Curve, and Mathews Correlation Coefficient metrics. The obtained results point out that the present system is able to recognize the selected throwing gestures and that the overall performance of the Kinect is better compared to the Pandlet.This article is a result of the project Deus Ex Machina: NORTE-01-0145-FEDER-000026, supported by Norte Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Supervised machine learning algorithms for ground motion time series classification from InSAR data

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the genera- tion of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deforma- tion identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/ AEI/10.13039/501100011033. Additionally, it has been supported by the European Regional Devel- opment Fund (ERDF) through the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme. Additionally, this work has been co-funded by the European Union Civil Protection through the H2020 project RASTOOL (UCPM-2021-PP-101048474).Peer ReviewedPostprint (published version