Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals.
A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions.
Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study's results could help improve coaching techniques and enhance batsmen's performance in cricket, ultimately improving the game's overall quality.
Cricket Sport , Humans , Algorithms , Machine Learning , Support Vector Machine
Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient's respiration rate. However, it is crucial to consider a patient's medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.
Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.
Breast Neoplasms , Microwaves , Humans , Female , Breast/diagnostic imaging , Breast Neoplasms/diagnostic imaging , Breast Neoplasms/pathology , Supervised Machine Learning , Technology
Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user's choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scene.
Hearing Aids , Hearing Loss , Speech Perception , Humans , Noise , Hearing Loss/therapy , Acoustics
BACKGROUND: Tyrosinemia type 1 (HT1) is a rare metabolic disorder caused by a defect in the tyrosine catabolic pathway. Since HT1 patients are treated with NTBC, outcome improved and life expectancy greatly increased. However extensive neurocognitive and behavioural problems have been described, which might be related to treatment with NTBC, the biochemical changes induced by NTBC, or metabolites accumulating due to the enzymatic defect characterizing the disease. OBJECTIVE: To study the possible pathophysiological mechanisms of brain dysfunction in HT1, we assessed blood and brain LNAA, and brain monoamine neurotransmitter metabolite levels in relation to behavioural and cognitive performance of HT1 mice. DESIGN: C57BL/6 littermates were divided in three different experimental groups: HT1, heterozygous and wild-type mice (n = 10; 5 male). All groups were treated with NTBC and underwent cognitive and behavioural testing. One week after behavioural testing, blood and brain material were collected to measure amino acid profiles and brain monoaminergic neurotransmitter levels. RESULTS: Irrespective of the genetic background, NTBC treatment resulted in a clear increase in brain tyrosine levels, whereas all other brain LNAA levels tended to be lower than their reference values. Despite these changes in blood and brain biochemistry, no significant differences in brain monoamine neurotransmitter (metabolites) were found and all mice showed normal behaviour and learning and memory. CONCLUSION: Despite the biochemical changes, NTBC and genotype of the mice were not associated with poorer behavioural and cognitive function of the mice. Further research involving dietary treatment of FAH-/- are warranted to investigate whether this reveals the cognitive impairments that have been seen in treated HT1 patients.
Nitrobenzoates , Tyrosinemias , Animals , Mice , Male , Cyclohexanones , Mice, Inbred C57BL , Tyrosinemias/drug therapy , Tyrosinemias/genetics , Tyrosine/metabolism
MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated.
Breast Neoplasms , Microwave Imaging , Algorithms , Breast/diagnostic imaging , Breast/pathology , Breast Neoplasms/diagnostic imaging , Breast Neoplasms/pathology , Diagnostic Imaging , Female , Humans , Microwaves , Neural Networks, Computer
Existing phenylalanine hydroxylase (PAH)-deficient mice strains are useful models of untreated or late-treated human phenylketonuria (PKU), as most contemporary therapies can only be initiated after weaning and the pups have already suffered irreversible consequences of chronic hyperphenylalaninemia (HPA) during early brain development. Therefore, we sought to evaluate whether enzyme substitution therapy with pegvaliase initiated near birth and administered repetitively to C57Bl/6-Pahenu2/enu2 mice would prevent HPA-related behavioral and cognitive deficits and form a model for early-treated PKU. The main results of three reported experiments are: 1) lifelong weekly pegvaliase treatment prevented the cognitive deficits associated with HPA in contrast to persisting deficits in mice treated with pegvaliase only as adults. 2) Cognitive deficits reappear in mice treated with weekly pegvaliase from birth but in which pegvaliase is discontinued at 3 months age. 3) Twice weekly pegvaliase injection also prevented cognitive deficits but again cognitive deficits emerged in early-treated animals following discontinuation of pegvaliase treatment during adulthood, particularly in females. In all studies, pegvaliase treatment was associated with complete correction of brain monoamine neurotransmitter content and with improved overall growth of the mice as measured by body weight. Mean total brain weight however remained low in all PAH deficient mice regardless of treatment. Application of enzyme substitution therapy with pegvaliase, initiated near birth and continued into adulthood, to PAH-deficient Pahenu2/enu2 mice models contemporary early-treated human PKU. This model will be useful for exploring the differential pathophysiologic effects of HPA at different developmental stages of the murine brain.
Phenylalanine Hydroxylase , Phenylketonurias , Adult , Animals , Cognition , Diet , Disease Models, Animal , Female , Humans , Mice , Mice, Inbred C57BL , Phenylalanine , Phenylalanine Ammonia-Lyase , Phenylalanine Hydroxylase/genetics , Phenylketonurias/drug therapy , Recombinant Proteins
Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human's voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors' knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.
Radar , Signal Processing, Computer-Assisted , Emotions , Female , Humans , Machine Learning , Male , Respiration
Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.
Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.
Algorithms , Neural Networks, Computer , Acoustics , Humans , Machine Learning , Support Vector Machine
Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.
Automobile Driving , Humans , Neural Networks, Computer , Respiratory Rate , Support Vector Machine , Wakefulness
In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0°, 90°, 180°, and 270°. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An "ideal/reference" image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed.
Artifacts , Hemorrhagic Stroke , Microwave Imaging , Algorithms , Hemorrhagic Stroke/diagnostic imaging , Humans , Phantoms, Imaging
Phenylalanine hydroxylase (PAH) deficiency, colloquially known as phenylketonuria (PKU), is among the most common inborn errors of metabolism and in the past decade has become a target for the development of novel therapeutics such as gene therapy. PAH deficient mouse models have been key to new treatment development, but all prior existing models natively express liver PAH polypeptide as inactive or partially active PAH monomers, which complicates the experimental assessment of protein expression following therapeutic gene, mRNA, protein, or cell transfer. The mutant PAH monomers are able to form hetero-tetramers with and inhibit the overall holoenzyme activity of wild type PAH monomers produced from a therapeutic vector. Preclinical therapeutic studies would benefit from a PKU model that completely lacks both PAH activity and protein expression in liver. In this study, we employed CRISPR/Cas9-mediated gene editing in fertilized mouse embryos to generate a novel mouse model that lacks exon 1 of the Pah gene. Mice that are homozygous for the Pah exon 1 deletion are viable, severely hyperphenylalaninemic, accurately replicate phenotypic features of untreated human classical PKU and lack any detectable liver PAH activity or protein. This model of classical PKU is ideal for further development of gene and cell biologics to treat PKU.
Liver/metabolism , Phenylalanine Hydroxylase/genetics , Phenylalanine/genetics , Phenylketonurias/therapy , Animals , CRISPR-Cas Systems/genetics , Disease Models, Animal , Exons/genetics , Gene Editing , Genetic Vectors/genetics , Genetic Vectors/pharmacology , Humans , Liver/drug effects , Liver/pathology , Mice , Phenylalanine/metabolism , Phenylalanine Hydroxylase/pharmacology , Phenylketonurias/genetics , Phenylketonurias/pathology
Phenylketonuria (PKU) due to recessively inherited phenylalanine hydroxylase (PAH) deficiency results in hyperphenylalaninemia, which is toxic to the central nervous system. Restriction of dietary phenylalanine intake remains the standard of PKU care and prevents the major neurologic manifestations of the disease, yet shortcomings of dietary therapy remain, including poor adherence to a difficult and unpalatable diet, an increased incidence of neuropsychiatric illness, and imperfect neurocognitive outcomes. Gene therapy for PKU is a promising novel approach to promote lifelong neurological protection while allowing unrestricted dietary phenylalanine intake. In this study, liver-tropic recombinant AAV2/8 vectors were used to deliver CRISPR/Cas9 machinery and facilitate correction of the Pah enu2 allele by homologous recombination. Additionally, a non-homologous end joining (NHEJ) inhibitor, vanillin, was co-administered with the viral drug to promote homology-directed repair (HDR) with the AAV-provided repair template. This combinatorial drug administration allowed for lifelong, permanent correction of the Pah enu2 allele in a portion of treated hepatocytes of mice with PKU, yielding partial restoration of liver PAH activity, substantial reduction of blood phenylalanine, and prevention of maternal PKU effects during breeding. This work reveals that CRISPR/Cas9 gene editing is a promising tool for permanent PKU gene editing.
Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy.
Breast/diagnostic imaging , Microwave Imaging , Neural Networks, Computer , Support Vector Machine , Algorithms , Breast Neoplasms/diagnostic imaging , Clinical Trials as Topic , Dielectric Spectroscopy/instrumentation , Dielectric Spectroscopy/methods , Equipment Design , Female , Humans , Magnetic Resonance Imaging , Mammography , ROC Curve , Scattering, Radiation , Statistics, Nonparametric , Ultrasonography, Mammary
Home monitoring and remote care systems aim to ultimately provide independent living care scenarios through non-intrusive, privacy-protecting means. Their main aim is to provide care through appreciating normal habits, remotely recognizing changes and acting upon those changes either through informing the person themselves, care providers, family members, medical practitioners, or emergency services, depending on need. Care giving can be required at any age, encompassing young to the globally growing aging population. A non-wearable and unobtrusive architecture has been developed and tested here to provide a fruitful health and wellbeing-monitoring framework without interfering in a user's regular daily habits and maintaining privacy. This work focuses on tracking locations in an unobtrusive way, recognizing daily activities, which are part of maintaining a healthy/regular lifestyle. This study shows an intelligent and locally based edge care system (ECS) solution to identify the location of an occupant's movement from daily activities using impulse radio-ultra wide band (IR-UWB) radar. A new method is proposed calculating the azimuth angle of a movement from the received pulse and employing radar principles to determine the range of that movement. Moreover, short-term fourier transform (STFT) has been performed to determine the frequency distribution of the occupant's action. Therefore, STFT, azimuth angle, and range calculation together provide the information to understand how occupants engage with their environment. An experiment has been carried out for an occupant at different times of the day during daily household activities and recorded with time and room position. Subsequently, these time-frequency outcomes, along with the range and azimuth information, have been employed to train a support vector machine (SVM) learning algorithm for recognizing indoor locations when the person is moving around the house, where little or no movement indicates the occurrence of abnormalities. The implemented framework is connected with a cloud server architecture, which enables to act against any abnormality remotely. The proposed methodology shows very promising results through statistical validation and achieved over 90% testing accuracy in a real-time scenario.
A more locally cared for and self-managing aging population along with better attention to self health-care, has resulted in increasing need for non-intrusive monitoring. Wearable, wireless physiological sensors, and cameras can pose user privacy, security and discomfort issues which may have a negative impact on consumer confidence and uptake. Thus, for the first time a non-contact, non-intrusive 3D human motion model is proposed for gait disorder identification from impulse radio ultra-wide band (ITERATOR) with the understanding of spherical trigonometry and vector field. Simultaneously, the Kinect Xbox One is used to compare the outcomes of the proposed IR-UWB model. The experiment comprises twenty-four human participants, where twenty people have normal walking pattern and four persons have spasticity. The height of different body sections from the ground have been recorded for each individual and employed later to distinguish lower and upper human body from the outcomes. The proposed work has transformed the radars backscattered responses through trigonometry and vector algebra where, only vector algebra has been implemented to transform the skeletal data obtained from Kinect. Angles between two thighs have been determined from the proposed UWB algorithm and validated against angles obtained from the Kinect skeletal data using root mean square error (RMSE), where less than 0.5 RMSE has been found.
Gait , Algorithms , Humans , Wireless Technology
Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.
