Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Improvement of the sensory and autonomous capability of robots through olfaction: the IRO Project

Proyecto de Excelencia Junta de Andalucía TEP2012-530Olfaction is a valuable source of information about the environment that has not been su ciently exploited in mobile robotics yet. Certainly, odor information can contribute to other sensing modalities, e.g. vision, to successfully accomplish high-level robot activities, such as task planning or execution in human environments. This paper describes the developments carried out in the scope of the IRO project, which aims at making progress in this direction by investigating mechanisms that exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems like object recognition and scene-activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decisionmaking processes. The results of the IRO project have improved the robot capabilities in terms of efciency, autonomy and usefulness.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Gas identification with tin oxide sensor array and self-organizing maps: adaptive correction of sensor drifts

Low-cost tin oxide gas sensors are inherently nonspecific. In addition, they have several undesirable characteristics such as slow response, nonlinearities, and long-term drifts. This paper shows that the combination of a gas-sensor array together with self-organizing maps (SOM's) permit success in gas classification problems. The system is able to determine the gas present in an atmosphere with error rates lower than 3%. Correction of the sensor's drift with an adaptive SOM has also been investigate

A real time classification algorithm for EEG-based BCI driven by self-induced emotions

Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities

Power spectral density estimation for wireless fluctuation enhanced gas sensor nodes

Fluctuation enhanced sensing (FES) is a promising method to improve the selectivity and sensitivity of semiconductor and nanotechnology gas sensors. Most measurement setups include high cost signal conditioning and data acquisition units as well as intensive data processing. However, there are attempts to reduce the cost and energy consumption of the hardware and to find efficient processing methods for low cost wireless solutions. In our paper we propose highly efficient signal processing methods to analyze the power spectral density of fluctuations. These support the development of ultra-low-power intelligent fluctuation enhanced wireless sensor nodes while several further applications are also possible

Emotional Brain-Computer Interfaces

Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs

Integrating Olfaction in a Robotic Telepresence Loop

In this work we propose enhancing a typical robotic telepresence architecture by considering olfactory and wind flow information in addition to the common audio and video channels. The objective is to expand the range of applications where robotics telepresence can be applied, including those related to the detection of volatile chemical substances (e.g. land-mine detection, explosive deactivation, operations in noxious environments, etc.). Concretely, we analyze how the sense of smell can be integrated in the telepresence loop, covering the digitization of the gases and wind flow present in the remote environment, the transmission through the communication network, and their display at the user location. Experiments under different environmental conditions are presented to validate the proposed telepresence system when localizing a gas emission leak at the remote environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification

Classifying odor in real experiment presents some challenges, especially the uncertainty of the odor concentration and dispersion that can lead to a difficulty in obtaining an accurate datasets. In this study, to enhance the accuracy, datasets arrangement based on MOS sensors parameters using SVM approach for odor classification is proposed. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method to see the sensors' performance in real situation. These performances are considered as the base of choosing the position in collecting the datasets. The sensors in dynamic experiment have average of precision of 93.8-97.0%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the collected datasets can support the SVM in improving the intelligent sensing when conducting odor classification work

Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions

Diabetic ketoacidosis is a complication of diabetes that occurs when body cannot produce insulin necessarily to convert glucose into energy, instead fat is used as energy source and produce ketone as a byproduct. Ketones can be detected in urine compounds, especially when there is a large number of ketones that produce a distinctive smell of acetone. Odor sensors assembled into Electrical Nose (E-nose) system is used as self-diagnostics pre-test for diabetic’s analysis. However, diabetic’s analysis often required a subject to fast before sample testing. Currently, different prediction model for diabetic ketoacidosis are used depending on fasting or non-fasting conditions. This is inconvenience for diabetic’s analysis to be done at any time anywhere. This project aim to propose an adaptive prediction model capable to diagnose diabetic ketoacidosis in unrestricted food intake conditions. The adaptive Neuro-fuzzy Inference System (ANFIS) is proposed to build the diabetic ketoacidosis classifier. The fuzzy inference model will be used to capture both fasting and non-fasting membership functions before feeding the results for classification to the neural network model. Two sets of experimental data involving 20 diabetic patients and 20 healthy subjects were collected from CITO laboratory Semarang Central Java, Indonesia. Ethics consents were informed and agreed by the subjects before starting the data collection. This project follows the experimental methodology in verifying the hypothesis drawn. The experimental paradigm was designed to simulate fasting and non-fasting conditions. Samples data were recorded in the morning before food intake and two hours after food intake using four MQ 2, MQ 5, MQ 6 and MQ 8 sensors, in previously built Electronic Nose prototype system. A 5-fold cross-validation testing was implemented for classification results analysis. The results are highly promising with at least 90% accuracy in all testing. The proposed model has achieved 96% average accuracy in unrestricted food intake conditions. The prediction results on non-fasting and fasting data samples were recorded as 98% and 96% of average accuracy respectively. This has proven that the proposed ANFIS model is good to detect diabetic’s cases through ketoacidosis regardless of food intake. It has better performance in normal food intake as compare to fasting condition, since insulin inefficiency happened in diabetics patients will resulted in obvious acetone secretion in nonfasting condition. The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. The proposed GA-ANFIS approach provides excellent classification in accuracy, precision and recall. However, the results are only a minor improvement from the non-optimized ANFIS model since the predecessor has achieved good classification accuracy. In conclusion, diabetic ketoacidosis in unrestricted food intake conditions can be predicted using the proposed ANFIS and GA-ANFIS model. Future work should be focusing on data collection of the E-Nose sensors and the improvement of the learning algorithm robustness towards environmental noise during data acquisition, such as evaporation and contamination of odor samples