Studio e implementazione di metodi per la classificazione automatica di movimenti umani basata su dati accelerometrici

Questo lavoro si pone come obiettivo lo studio di algoritmi per la classificazione automatica di posture e movimenti eseguiti da un soggetto, mediante elaborazione dei segnali provenienti da cinque accelerometri biassiali posti in corrispondenza di determinati punti anatomici. Un sistema di classificazione automatica del movimento è di grande interesse in applicazioni di pervasive computing che richiedano la conoscenza del contesto per facilitare l’interazione uomo-macchina, e in biomedicina, per la realizzazione di sistemi wearable per la valutazione a lungo-termine di parametri fisiologici e biomeccanici. In questo lavoro ci proponiamo in primo luogo di studiare algoritmi di classificazione one-shot, in cui l’esito della classificazione a un certo istante non dipende dalla storia delle classificazioni precedenti, e algoritmi di classificazione sequenziale basati sugli Hidden Markov Model (HMM), per sfruttare la conoscenza delle statistiche di un task risultante dal concatenamento di singole primitive di movimento. All’algoritmo di classificazione automatica delle sequenze di movimenti e posture è stato inoltre introdotto un sistema di rimozione automatica dei dati non classificabili, relativi alle transizioni posturali o ai movimenti non noti al sistema. The aim of this study is the development of an algorithm for automatic classification of human postures and movements, starting from accelerometer data. The acceleration data can be measured by a few sensors affixed to selected points of the human body. Movement classifiers can be interesting in applications of pervasive computing, whereas contextual awareness may ease the human-machine interaction, or in biomedicine, whereas wearable systems are developed for long-term monitoring of physiological and biomechanical parameters. In this paper we intend to study one-shot and sequential classifiers. One-shot classifiers deliver their actual outcome, without any regard to previous outcomes. Conversely, sequential classifiers, i.e. Hidden Markov Model (HMM), incorporate the statistical information acquired about the movement dynamics into the classification process. An automatic spurious data removing algorithm has been added to this kind of classifier, to make possible the automatic detection and removal of data relative to unknown movements or postural transitions

Accelerometry-Based Classification of Human Activities Using Markov Modeling

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series

Optimal Spatial Sensor Design for Magnetic Tracking in a Myokinetic Control Interface.

Abstract Background and Objectives Magnetic tracking involves the use of magnetic sensors to localize one or more magnetic objectives, in those applications in which a free line-of-sight between them and the operator is hampered. We applied this concept to prosthetic hands, which could be controlled by tracking permanent magnets implanted in the forearm muscles of amputees (the myokinetic control interface). Concerning the system design, the definition of a sensor distribution which maximizes the information, while minimizing the computational cost of localization, is still an open problem. We present a simple yet effective strategy to define an optimal sensor set for tracking multiple magnets, which we called the Peaks method. Methods We simulated a proximal amputation using a 3D CAD model of a human forearm, and the implantation of 11 magnets in the residual muscles. The Peaks method was applied to select a subset of sensors from an initial grid of 480 elements. The approach involves setting an appropriate threshold to select those sensors associated with the peaks in the magnetic flux density and its gradient distributions. Selected sensors were used to track the magnets during muscle contraction. For validating our strategy, an alternative method based on state-of-the-art solutions was implemented. We finally proposed a calibration phase to customize the sensor distribution on the specific patient's anatomy. Results 80 sensors were selected with the Peaks method, and 101 with the alternative one. A localization accuracy below 0.22 mm and 1.86° for position and orientation, respectively, was always achieved. Unlike alternative methods from the literature, neither iterative or analytical solution, nor a-priori knowledge on the magnet positions or trajectories were required, and yet the outcomes achieved with the two strategies proved statistically comparable. The calibration phase proved useful to adapt the sensors to the patient's stump and to increase the signal-to-noise ratio against intrinsic noise. Conclusions We demonstrated an efficient and general solution for solving the design optimization problem (i.e. identifying an optimal sensor set) and reducing the computational cost of localization. The optimal sensor distribution mirrors the field shape traced by the magnets on the sensing surface, being an intuitive and fast way of achieving the same results of more complex and application-specific methods. Several applications in the (bio)medical field involving magnetic tracking will benefit from the outcomes of this work

A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients

Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington’s disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist. By extracting features from group-specific Hidden Markov Models (HMMs) and signal information in time and frequency domain, a Support Vector Machines classifier (SVM) was designed and validated. The 90.5% of subjects was assigned to the right group after leave-one-subject-out cross validation and majority voting. The long-term goal we point to is the gait assessment in everyday life to early detect gait alterations

Comparing pre- and post-pandemic greenhouse gas and noise emissions from road traffic in Rome (Italy): a multi-step approach

This study presents the results of a traffic simulation analysis and emissions (greenhouse gas and noise) assessment comparing pre-pandemic (2019) and post-pandemic (2022) periods. The estimation of road traffic demand is based on conventional data sources and floating car data; next, the traffic simulation procedure was performed providing road network traffic volumes, which are the input for the emission models. The diffusion of teleworking, e-commerce, as well as the digitization of many processes, services and activities, lead to a significant change in urban mobility. Results show a significant though still not complete resumption of commuters travel activity (−10% compared to pre-pandemic period) in the morning peak-hour. This translates into an 11% reduction of greenhouse gas emissions and a 0.1% increase in noise emissions

Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory

This paper describes a general approach to the specification the stable regime speed-flow function, for motorways, as a part of the stable regime Stochastic Fundamental Diagram consistent with main assumptions of Transportation Systems Theory. Main original elements are: • Specification of speed-flow functions consistent with travel time function, such as BPR-like functions; • Calibration from disaggregate data, say data from single vehicle trajectories; • Specification of the speed r. v. distribution consistent with those used in RUT for route choice behavior modelling, such as Gamma, Inv-Gamma

Grasp force estimation from the transient EMG using high-density surface recordings.

Objective: Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force was so far overlooked. Approach: High Density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final grasp force was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R2), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the grasp force (GF) onset were compared to determine the time at which the grasp force can be ascertained from the EMG signals. Main results: The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the grasp force through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500ms of data following the onset. Significance: The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final grasp force. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal

Improvement prostate cancer detection rate of suspicious lesions through MRI/TRUS fusion guided biopsy by a multiteam of radiologists

Abstract Background The objective of our study was to analyze the data of our biopsies, determine a detection rate (DR), compare it with the data in the literature and draw possible deductions, so as to offer the patient the possibility of not having other biopsies in the future. Methods We have enrolled 189 biopsy-naive patients in the period between September 2018 and December 2020. Each patient underwent multiparametric (mp)-MRI which was reviewed by our team of radiologists. In our center, each examination is examined by 4 radiologists separately with an overall final result. Through the t student test, any statistically significant differences between the DRs and the concordance rate between the positive cores and the suspected area on MRI were analyzed for each urologist who performed the procedure. Results The absolute (DR) was 69.3% (131/189 patients). The relative DR for each PIRADS score was 41% for PIRADS 3, 70.2% for PIRADS 4, 89.3% for PIRADS 5. We found a high percentage of agreement between the positive biopsy samples and the suspicious area identified on MRI: 90.8% (119/131 patients). There were no statistically significant differences between the DRs of the urologists who performed the procedure (p = 0.89), nor for the percentage of agreement between the positivity of the core and the suspected area on MRI (p = 0.92). Conclusions MRI in the future could become the gold standard for performing MRI fusion-guided biopsies to have a better diagnostic result and avoid rebiopsies. A team MRI reading allows greater accuracy in identifying the suspected lesion, which is demonstrated by a high rate of agreement with the positivity of the cores (90.8%). There is a cost problem due to the need to carry out the mpMRI but it could have less impact in the future. In addition, the MRI provides useful information on the extent of the disease (e.g., cT3a/b) which allows you to better plan the surgical strategy or other therapies

a smartwatch step counter for slow and intermittent ambulation

The ambulatory monitoring of human movement can provide valuable information regarding the degree of functional ability and general level of activity of individuals. Since walking is a basic everyday movement, automatic step detection or step counting is very important in developing ambulatory monitoring systems. This paper is concerned with the development and the preliminary validation of a step counter (SC) designed to operate also in conditions of slow and intermittent ambulation. The SC was based on processing the accelerometer data measured by a Gear 2 smartwatch running a custom wearable app, named ADAM. A data set of eight users, for a total of 80 trials, was used to tune ADAM. Finally, ADAM was compared with two different commercial SCs: the native SC running on the Gear 2 smart watch and a waist-worn SC, the Geonaute ONSTEP 400. A second data set of eight additional users for a total of 80 trials was used for the assessment study. The three SCs performed quite similarly in conditions of normal walking over long paths (1%–3% of mean absolute relative error); ADAM outperformed the two other SCs in conditions of slow and intermittent ambulation; the error incurred by ADAM was limited to 5%, which is significantly lower than errors of 20%–30% incurred by the two other SCs