Fuzzy Entropy-Based Spatial Hotspot Reliability.

Cluster techniques are used in hotspot spatial analysis to detect hotspots as areas on the map; an extension of the Fuzzy C-means that the clustering algorithm has been applied to locate hotspots on the map as circular areas; it represents a good trade-off between the accuracy in the detection of the hotspot shape and the computational complexity. However, this method does not measure the reliability of the detected hotspots and therefore does not allow us to evaluate how reliable the identification of a hotspot of a circular area corresponding to the detected cluster is; a measure of the reliability of hotspots is crucial for the decision maker to assess the need for action on the area circumscribed by the hotspots. We propose a method based on the use of De Luca and Termini's Fuzzy Entropy that uses this extension of the Fuzzy C-means algorithm and measures the reliability of detected hotspots. We test our method in a disease analysis problem in which hotspots corresponding to areas where most oto-laryngo-pharyngeal patients reside, within a geographical area constituted by the province of Naples, Italy, are detected as circular areas. The results show a dependency between the reliability and fluctuation of the values of the degrees of belonging to the hotspots.

A New Validity Index Based on Fuzzy Energy and Fuzzy Entropy Measures in Fuzzy Clustering Problems.

Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then, it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI (University of California at Irvine) machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of fuzzy C-means by using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption.

Seasonal Time Series Forecasting by F1-Fuzzy Transform.

We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series' trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.

A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation.

BACKGROUND: Unfortunately, global efforts to promote "how much" physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets. OBJECTIVE: The aim of this study was to develop and implement an innovative algorithm to determine "where" physical activity occurs using proximity sensors coupled with a widely used physical activity monitor. METHODS: A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters. RESULTS: Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R2=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R2=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer. CONCLUSIONS: This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of "context sensing" will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction).

Evaluation of a Sensor System for Detecting Humans Trapped under Rubble: A Pilot Study.

Rapid localization of injured survivors by rescue teams to prevent death is a major issue. In this paper, a sensor system for human rescue including three different types of sensors, a CO2 sensor, a thermal camera, and a microphone, is proposed. The performance of this system in detecting living victims under the rubble has been tested in a high-fidelity simulated disaster area. Results show that the CO2 sensor is useful to effectively reduce the possible concerned area, while the thermal camera can confirm the correct position of the victim. Moreover, it is believed that the use of microphones in connection with other sensors would be of great benefit for the detection of casualties. In this work, an algorithm to recognize voices or suspected human noise under rubble has also been developed and tested.

Energy and Entropy Measures of Fuzzy Relations for Data Analysis.

We present a new method for assessing the strength of fuzzy rules with respect to a dataset, based on the measures of the greatest energy and smallest entropy of a fuzzy relation. Considering a fuzzy automaton (relation), in which A is the input fuzzy set and B the output fuzzy set, the fuzzy relation R1 with greatest energy provides information about the greatest strength of the input-output, and the fuzzy relation R2 with the smallest entropy provides information about uncertainty of the input-output relationship. We consider a new index of the fuzziness of the input-output based on R1 and R2. In our method, this index is calculated for each pair of input and output fuzzy sets in a fuzzy rule. A threshold value is set in order to choose the most relevant fuzzy rules with respect to the data.

Anatomical Calibration through Post-Processing of Standard Motion Tests Data.

The inertial measurement unit is popularly used as a wearable and flexible tool for human motion tracking. Sensor-to-body alignment, or anatomical calibration (AC), is fundamental to improve accuracy and reliability. Current AC methods either require extra movements or are limited to specific joints. In this research, the authors propose a novel method to achieve AC from standard motion tests (such as walking, or sit-to-stand), and compare the results with the AC obtained from specially designed movements. The proposed method uses the limited acceleration range on medial-lateral direction, and applies principal component analysis to estimate the sagittal plane, while the vertical direction is estimated from acceleration during quiet stance. The results show a good correlation between the two sets of IMUs placed on frontal/back and lateral sides of head, trunk and lower limbs. Moreover, repeatability and convergence were verified. The AC obtained from sit-to-stand and walking achieved similar results as the movements specifically designed for upper and lower body AC, respectively, except for the feet. Therefore, the experiments without AC performed can be recovered through post-processing on the walking and sit-to-stand data. Moreover, extra movements for AC can be avoided during the experiment and instead achieved through the proposed method.

Quantitative Laughter Detection, Measurement, and Classification-A Critical Survey.

The study of human nonverbal social behaviors has taken a more quantitative and computational approach in recent years due to the development of smart interfaces and virtual agents or robots able to interact socially. One of the most interesting nonverbal social behaviors, producing a characteristic vocal signal, is laughing. Laughter is produced in several different situations: in response to external physical, cognitive, or emotional stimuli; to negotiate social interactions; and also, pathologically, as a consequence of neural damage. For this reason, laughter has attracted researchers from many disciplines. A consequence of this multidisciplinarity is the absence of a holistic vision of this complex behavior: the methods of analysis and classification of laughter, as well as the terminology used, are heterogeneous; the findings sometimes contradictory and poorly documented. This survey aims at collecting and presenting objective measurement methods and results from a variety of different studies in different fields, to contribute to build a unified model and taxonomy of laughter. This could be successfully used for advances in several fields, from artificial intelligence and human-robot interaction to medicine and psychiatry.

Reliability of the step phase detection using inertial measurement units: pilot study.

The use of inertial sensors for the gait event detection during a long-distance walking, for example, on different surfaces and with different walking patterns, is important to evaluate the human locomotion. Previous studies demonstrated that gyroscopes on the shank or foot are more reliable than accelerometers and magnetometers for the event detection in case of normal walking. However, these studies did not link the events with the temporal parameters used in the clinical practice; furthermore, they did not clearly verify the optimal position for the sensors depending on walking patterns and surface conditions. The event detection quality of the sensors is compared with video, used as ground truth, according to the parameters proposed by the Gait and Clinical Movement Analysis Society. Additionally, the performance of the sensor on the foot is compared with the one on the shank. The comparison is performed considering both normal walking and deviations to the walking pattern, on different ground surfaces and with or without constraints on movements. The preliminary results show that the proposed methodology allows reliable detection of gait events, even in case of abnormal footfall and in slipping surface conditions, and that the optimal location to place the sensors is the shank.

Development of subliminal persuasion system to improve the upper limb posture in laparoscopic training: a preliminary study.

PURPOSE: Current training for laparoscopy focuses only on the enhancement of manual skill and does not give advice on improving trainees' posture. However, a poor posture can result in increased static muscle loading, faster fatigue, and impaired psychomotor task performance. In this paper, the authors propose a method, named subliminal persuasion, which gives the trainee real-time advice for correcting the upper limb posture during laparoscopic training like the expert but leads to a lower increment in the workload. METHODS: A 9-axis inertial measurement unit was used to compute the upper limb posture, and a Detection Reaction Time device was developed and used to measure the workload. A monitor displayed not only images from laparoscope, but also a visual stimulus, a transparent red cross superimposed to the laparoscopic images, when the trainee had incorrect upper limb posture. One group was exposed, when their posture was not correct during training, to a short (about 33 ms) subliminal visual stimulus. The control group instead was exposed to longer (about 660 ms) supraliminal visual stimuli. RESULTS: We found that subliminal visual stimulation is a valid method to improve trainees' upper limb posture during laparoscopic training. Moreover, the additional workload required for subconscious processing of subliminal visual stimuli is less than the one required for supraliminal visual stimuli, which is processed instead at the conscious level. CONCLUSIONS: We propose subliminal persuasion as a method to give subconscious real-time stimuli to improve upper limb posture during laparoscopic training. Its effectiveness and efficiency were confirmed against supraliminal stimuli transmitted at the conscious level: Subliminal persuasion improved upper limb posture of trainees, with a smaller increase on the overall workload.

Objective evaluation of oral presentation skills using Inertial Measurement Units.

Oral presentation is considered as one of the most sought after skills by companies and professional organizations and program accreditation agencies. However, both learning process and evaluation of this skill are time demanding and complex tasks that need dedication and experience. Furthermore, the role of the instructor is fundamental during the presentation assessment. The instructor needs to consider several verbal and nonverbal communications cues sent in parallel and this kind of evaluation is often subjective. Even if there are oral presentation rubrics that try to standardize the evaluation, they are not an optimal solution because they do not provide the presenter a real-time feedback. In this paper, we describe a system for behavioral monitoring during presentations. We propose an ecological measurement system based on Inertial Measurement Units to evaluate objectively the presenter's posture through objective parameters. The system can be used to provide a real-time feedback to the presenters unobtrusively.

Objective skill evaluation for laparoscopic training based on motion analysis.

Performing laparoscopic surgery requires several skills, which have never been required for conventional open surgery. Surgeons experience difficulties in learning and mastering these techniques. Various training methods and metrics have been developed to assess and improve surgeon's operative abilities. While these training metrics are currently widely being used, skill evaluation methods are still far from being objective in the regular laparoscopic skill education. This study proposes a methodology of defining a processing model that objectively evaluates surgical movement performance in the routine laparoscopic training course. Our approach is based on the analysis of kinematic data describing the movements of surgeon's upper limbs. An ultraminiaturized wearable motion capture system (Waseda Bioinstrumentation system WB-3), therefore, has been developed to measure and analyze these movements. The data processing model was trained by using the subjects' motion features acquired from the WB-3 system and further validated to classify the expertise levels of the subjects with different laparoscopic experience. Experimental results show that the proposed methodology can be efficiently used both for quantitative assessment of surgical movement performance, and for the discrimination between expert surgeons and novices.

Development of the wireless ultra-miniaturized inertial measurement unit WB-4: preliminary performance evaluation.

This paper presents the preliminary performance evaluation of our new wireless ultra-miniaturized inertial measurement unit (IMU) WB-4 by compared with the Vicon motion capture system. The WB-4 IMU primarily contains a mother board for motion sensing, a Bluetooth module for wireless data transmission with PC, and a Li-Polymer battery for power supply. The mother board is provided with a microcontroller and 9-axis inertial sensors (miniaturized MEMS accelerometer, gyroscope and magnetometer) to measure orientation. A quaternion-based extended Kalman filter (EKF) integrated with an R-Adaptive algorithm for automatic estimation of the measurement covariance matrix is implemented for the sensor fusion to retrieve the attitude. The experimental results showed that the wireless ultra-miniaturized WB-4 IMU could provide high accuracy performance at the angles of roll and pitch. The yaw angle which has reasonable performance needs to be further evaluated.

Surface EMG and heartbeat analysis preliminary results in surgical training: dry boxes and live tissue.

The training in the surgical practice is of paramount importance to prepare the residents in performing surgical procedures on human subject and to provide exercise on new techniques for experienced surgeons. Usually, these trainings are carried out on live animals or in virtual environments and dry boxes; the complexity of the exercises is identical in both of the case, but the pressure in operating with a living subject could change the attitude and the movements of the trainee. Until now, it has not been possible to analyze this stress in details together in the surgical animal training and dry boxes. In this work we propose an innovative portable system that can measure two physiological parameters, the heartbeat and the surface electromyography, during a session of training in both of the environment. The preliminary results, for one subject, show a bigger average power in the shoulder muscles during the living operation together with a higher but stable heartbeat rate.

Development of the ultra-miniaturized Inertial Measurement Unit WB3 for objective skill analysis and assessment in neurosurgery: preliminary results.

In recent years there has been an ever increasing amount of research and development of technologies and methodologies aimed at improving the safety of advanced surgery. In this context, several training methods and metrics have been proposed, in particular for laparoscopy, both to improve the surgeon's abilities and also to assess her/his skills. For neurosurgery, however, the extremely small movements and sizes involved have prevented until now the development of similar methodologies and systems. In this paper we present the development of the ultra-miniaturized Inertial Measurement Unit WB3 (at present the smallest, lightest, and best performing in the world) for practical application in neurosurgery as skill assessment tool. This paper presents the feasibility study for quantitative discrimination of movements of experienced surgeons and beginners in a simple pick and place scenario.

Neural networks in astronomy.

In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).