ReadingAct RGB-D action dataset and human action recognition from local features

For general home monitoring, a system should automatically interpret people’s actions. The system should be non-intrusive, and able to deal with a cluttered background, and loose clothes. An approach based on spatio-temporal local features and a Bag-of-Words (BoW) model is proposed for single-person action recognition from combined intensity and depth images. To restore the temporal structure lost in the traditional BoW method, a dynamic time alignment technique with temporal binning is applied in this work, which has not been previously implemented in the literature for human action recognition on depth imagery. A novel human action dataset with depth data has been created using two Microsoft Kinect sensors. The ReadingAct dataset contains 20 subjects and 19 actions for a total of 2340 videos. To investigate the effect of using depth images and the proposed method, testing was conducted on three depth datasets, and the proposed method was compared to traditional Bag-of-Words methods. Results showed that the proposed method improves recognition accuracy when adding depth to the conventional intensity data, and has advantages when dealing with long actions.

Study of optimization for cooperative motion between two humans in mutual tracking

Verbal communication is essential for human society and human civilization. Non-verbal communication, on the other hand, is more widely used not only by human but also other kind of animals, and the content of information is estimated even larger than the verbal communication. Among the non-verbal communication mutual motion is the simplest and easiest to study experimentally and analytically. We measured the power spectrum of the hand velocity in various conditions and clarified the following points on the feed-back and feed- forward mechanism as basic knowledge to understand the condition of good communication.

Visual processing of optic flow and motor control in the human posterior cingulate sulcus

Previous studies have shown that the human posterior cingulate contains a visual processing area selective for optic flow (CSv). However, other studies performed in both humans and monkeys have identified a somatotopic motor region at the same location (CMA). Taken together, these findings suggested the possibility that the posterior cingulate contains a single visuomotor integration region. To test this idea we used fMRI to identify both visual and motor areas of the posterior cingulate in the same brains and to test the activity of those regions during a visuomotor task. Results indicated that rather than a single visuomotor region the posterior cingulate contains adjacent but separate motor and visual regions. CSv lies in the fundus of the cingulate sulcus, while CMA lies in the dorsal bank of the sulcus, slightly superior in terms of stereotaxic coordinates. A surprising and novel finding was that activity in CSv was suppressed during the visuomotor task, despite the visual stimulus being identical to that used to localize the region. This may provide an important clue to the specific role played by this region in the utilization of optic flow to control self-motion.

Wireless energy behaviour monitoring (Wi-be) for office buildings

This paper presents a study on reduction of energy consumption in buildings through behaviour change informed by wireless monitoring systems for energy, environmental conditions and people positions. A key part to the Wi-Be system is the ability to accurately attribute energy usage behaviour to individuals, so they can be targeted with specific feedback tailored to their preferences. The use of wireless technologies for indoor positioning was investigated to ascertain the difficulties in deployment and potential benefits. The research to date has demonstrated the effectiveness of highly disaggregated personal-level data for developing insights into people’s energy behaviour and identifying significant energy saving opportunities (up to 77% in specific areas). Behavioural research addressed social issues such as privacy, which could affect the deployment of the system. Radio-frequency research into less intrusive technologies indicates that received-signal-strength-indicator-based systems should be able to detect the presence of a human body, though further work would be needed in both social and engineering areas.

Could in-home sensors surpass human observation of people with Parkinson’s at high risk of falling? An ethnographic study

Self-report underpins our understanding of falls among people with Parkinson’s (PwP) as they largely happen unwitnessed at home. In this qualitative study, we used an ethnographic approach to investigate which in-home sensors, in which locations, could gather useful data about fall risk. Over six weeks, we observed five independently mobile PwP at high risk of falling, at home. We made field notes about falls (prior events and concerns) and recorded movement with video, Kinect, and wearable sensors. The three women and two men (aged 71 to 79 years) having moderate or severe Parkinson’s were dependent on others and highly sedentary. We most commonly noted balance protection, loss, and restoration during chair transfers, walks across open spaces and through gaps, turns, steps up and down, and tasks in standing (all evident walking between chair and stairs, e.g.). Our unobtrusive sensors were acceptable to participants: they could detect instability during everyday activity at home and potentially guide intervention. Monitoring the route between chair and stairs is likely to give information without invading the privacy of people at high risk of falling, with very limited mobility, who spend most of the day in their sitting rooms.

Factors associated with increased prevalence of human papillomavirus infection in a cohort of HIV-Infected Brazilian women

Objectives: Human papillomavirus (HPV) infection is a major risk factor for cervical disease. Using baseline data from the HIV-infected cohort of Evandro Chagas Clinical Research Institute at Fiocruz, Rio de Janeiro, Brazil, factors associated with an increased prevalence of HPV were assessed. Methods: Samples from 634 HIV-infected women were tested for the presence of HPV infection using hybrid capture 11 and polymerase chain reaction. Prevalence ratios (PR) were estimated using Poisson regression analysis with robust variance. Results: The overall prevalence of HPV infection was 48%, of which 94% were infected with a high-risk HPV. In multivariate analysis, factors independently associated with infection with high-risk HPV type were: younger age (<30 years of age; PR 1.5, 95% confidence interval (CI) 1.1-2.1), current or prior drug use (PR 1.3, 95% CI 1.0-1.6), self-reported history of HPV infection (PR 1.2, 95% CI 0.96-1.6), condom use in the last sexual intercourse (PR 1.3, 95% CI 1.1-1.7), and nadir CD4+ T-cell count <100 cells/mm(3) (PR 1.6, 95% CI 1.2-2.1). Conclusions: The estimated prevalence of high-risk HPV-infection among HIV-infected women from Rio de Janeiro, Brazil, was high. Close monitoring of HPV-related effects is warranted in all HIV-infected women, in particular those of younger age and advanced immunosuppression. (C) 2008 International Society for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Comparative bioavailability of three ibuprofen formulations in healthy human volunteers

Objective: To assess the bioequivalence of three ibuprofen formulations (Test formulation: ibuprofen (400 mg capsule) manufactured by Cardinal Health Brasil 402 Ltda. (Sorocaba, Brazil) and licensed to Boehringer Ingelheim do Brasil Quim. e Farm. Ltda. (Sao Paulo, Brazil); Reference formulation (1): ibuprofen (Advil (R); 2 x 200 mg coated tablet) from Wyeth-Whitehall Ltda. (Itapevi, Brazil); Reference formulation (2): ibuprofen (Alivium (R); 8 ml x 50 mg/ml solution) from Schering Plough S.A. (Rio de Janeiro, Brazil)) in 24 healthy volunteers of both sexes. Methods: The study was conducted using an open, randomized, three-period crossover design with at least 5-day washout interval. Plasma samples were obtained over a 24-h period. Plasma ibuprofen concentrations were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) with negative ion electrospray ionization using multiple reaction monitoring (MRM). The following pharmacokinetic parameters were obtained from the ibuprofen plasma concentration vs. time curves: AUC(last), AUC(trunctmax) AUC(inf) and C-max. Results: The limit of quantification for ibuprofen was 0.1 mu g x ml(-1). The geometric mean with corresponding 90% confidence interval (CI) for Test/Reference (1) percent ratios were 114.24% (90% CI = 105.67, 123.50%) for C-max, 98.97% (90% CI = 94.69, 103.44%) for AUC(last) and 99.40% (90% CI = 95.21, 103.78%) for AUCinf. The geometric mean and respective 90% confidence interval (CI) for Test/Reference (2) percent ratios were 108.38% (90% Cl = 100.195, 117.25%) for C-max, 100.79% (90% CI = 96.39, 105.40%) for AUC(last) and 101.26% (90% CI = 96.94, 105.77%) for AUC(inf); t(max) for the 400 mg Test capsule was shorter than that for the 2 x 200 mg Reference (1) tablets (p < 0.002). Conclusion: Since the 90% CI for AUC(last), AUC(inf) and C-max ratios were within the 80 - 125% interval proposed by the US FDA, it was concluded that ibuprofen formulation manufactured by Cardinal Health Brasil 402 Ltda. and licensed to Boehringer Ingelheim do Brasil Quim. e Farm. Ltda. is bioequivalent to the Advil (R) and Alivium (R) formulations with regard to both the rate and the extent of absorption.

Fluoxetine and norfluoxetine analysis by direct injection of human plasma in a column switching liquid chromatographic system

A column switching LC method is presented for the analysis of fluoxetine (FLU) and norfluoxetine (NFLU) by direct injection of human plasma using a lab-made restricted access media (RAM) column. A RAM-BSA-octadecyl silica (C-18) column (40 min x 4.6 mm, 10 mu m) is evaluated in both backflush and foreflush elution modes and coupled with a C-18 lab-made (50 mm x 4.6 mm, 3 pm) analytical column in order to perform online sample preparation. Direct injection of 100 mu L, of plasma samples is possible with the developed approach. In addition, reduction of sample handling is obtained when compared with traditional liquid-liquid extraction (LLE) and SPE. The total analysis time is around 20 min. A LOQ of 15 ng/mL is achieved in a concentration range of 15-500 ng/mL, allowing the therapeutic drug monitoring of clinical samples. The precision values achieved are lower than 15% for all the evaluated points with adequate recovery and accuracy. Furthermore, no matrix interferences are found in the analysis and the proposed method shows to be an adequate alternative for analysis of FLU in plasma.

Motion Detection for Video Surveillance

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Monitoring vegetation on railway embankments : supporting maintenance decisions

The national railway administrations in Scandinavia, Germany, and Austria mainly resort to manual inspections to control vegetation growth along railway embankments. Manually inspecting railways is slow and time consuming. A more worrying aspect concerns the fact that human observers are often unable to estimate the true cover of vegetation on railway embankments. Further human observers often tend to disagree with each other when more than one observer is engaged for inspection. Lack of proper techniques to identify the true cover of vegetation even result in the excess usage of herbicides; seriously harming the environment and threating the ecology. Hence work in this study has investigated aspects relevant to human variationand agreement to be able to report better inspection routines. This was studied by mainly carrying out two separate yet relevant investigations.First, thirteen observers were separately asked to estimate the vegetation cover in nine imagesacquired (in nadir view) over the railway tracks. All such estimates were compared relatively and an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05). Bearing in difference between the observers, a second follow-up field-study on the railway tracks was initiated and properly investigated. Two railway segments (strata) representingdifferent levels of vegetationwere carefully selected. Five sample plots (each covering an area of one-by-one meter) were randomizedfrom each stratumalong the rails from the aforementioned segments and ten images were acquired in nadir view. Further three observers (with knowledge in the railway maintenance domain) were separately asked to estimate the plant cover by visually examining theplots. Again an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05) confirming the result from the first investigation.The differences in observations are compared against a computer vision algorithm which detects the "true" cover of vegetation in a given image. The true cover is defined as the amount of greenish pixels in each image as detected by the computer vision algorithm. Results achieved through comparison strongly indicate that inconsistency is prevalent among the estimates reported by the observers. Hence, an automated approach reporting the use of computer vision is suggested, thus transferring the manual inspections into objective monitored inspections

Motion cue analysis for parkinsonian gait recognition

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

Enteric coated magnetic HPMC capsules evaluated in human gastrointestinal tract by AC biosusceptometry

Purpose. To employ the AC Biosusceptometry (ACB) technique to evaluate in vitro and in vivo characteristics of enteric coated magnetic hydroxypropyl methylcellulose (HPMC) capsules and to image the disintegration process.Materials and Methods. HPMC capsules filled with ferrite (MnFe2O4) and coated with Eudragit (R) were evaluated using USP XXII method and administered to fasted volunteers. Single and multisensor ACB systems were used to characterize the gastrointestinal (GI) motility and to determine gastric residence time (GRT), small intestinal transit time (SITT) and orocaecal transit time (OCTT). Mean disintegration time (t (50)) was quantified from 50% increase of pixels in the imaging area.Results. In vitro and in vivo performance of the magnetic HPMC capsules as well as the disintegration process were monitored using ACB systems. The mean disintegration time (t (50)) calculated for in vitro was 25 +/- 5 min and for in vivo was 13 +/- 5 min. In vivo also were determined mean values for GRT (55 +/- 19 min), SITT (185 +/- 82 min) and OCTT (240 +/- 88 min).Conclusions. AC Biosusceptometry is a non-invasive technique originally proposed to monitoring pharmaceutical dosage forms orally administered and to image the disintegration process.

Viable human buccal mucosa cells do not yield typical nucleoids: Impacts on the single-cell gel electrophoresis/comet assay

Buccal mucosa (BM) cells have been used in human biomonitoring studies for detecting DNA adducts and chromosomal damage in an epithelial cell population. In the present study, we have investigated if human BM cells are suitable for use in the single-cell gel electrophoresis (SCGE)/Comet assay as an approach for estimating the exposure of epithelial cells to DNA-damaging agents. Our results indicate that only a few cells from BM cell samples yield comets that can be analyzed by current methods, and that the yield of cells with comets is independent of the percentage of viable BM cells in the sample. Data generated after enzymatic enrichment of viable cells and immunomagnetic separation of epithelial cells suggest that most of the BM cells that do form comets are probably leukocytes. Moreover, by reevaluating specific cells after running the Comet assay, we found that viable epithelial BM cells give rise to atypical comets that are not included in the analysis. Comparing DNA migration patterns between small groups of smokers and nonsmokers indicated that long-term smoking had no effect on the subpopulation of cells that yield typical comets. Our results indicate that the SCGE assay, as it is commonly performed, may not be useful for genotoxicity monitoring in human epithelial BM cells.