The selection and evaluation of a sensory technology for interaction in a warehouse environment

In recent years, Human-Computer Interaction (HCI) has become a significant part of modern life as it has improved human performance in the completion of daily tasks in using computerised systems. The increase in the variety of bio-sensing and wearable technologies on the market has propelled designers towards designing more efficient, effective and fully natural User-Interfaces (UI), such as the Brain-Computer Interface (BCI) and the Muscle-Computer Interface (MCI). BCI and MCI have been used for various purposes, such as controlling wheelchairs, piloting drones, providing alphanumeric inputs into a system and improving sports performance. Various challenges are experienced by workers in a warehouse environment. Because they often have to carry objects (referred to as hands-full) it is difficult to interact with traditional devices. Noise undeniably exists in some industrial environments and it is known as a major factor that causes communication problems. This has reduced the popularity of using verbal interfaces with computer applications, such as Warehouse Management Systems. Another factor that effects the performance of workers are action slips caused by a lack of concentration during, for example, routine picking activities. This can have a negative impact on job performance and allow a worker to incorrectly execute a task in a warehouse environment. This research project investigated the current challenges workers experience in a warehouse environment and the technologies utilised in this environment. The latest automation and identification systems and technologies are identified and discussed, specifically the technologies which have addressed known problems. Sensory technologies were identified that enable interaction between a human and a computerised warehouse environment. Biological and natural behaviours of humans which are applicable in the interaction with a computerised environment were described and discussed. The interactive behaviours included the visionary, auditory, speech production and physiological movement where other natural human behaviours such paying attention, action slips and the action of counting items were investigated. A number of modern sensory technologies, devices and techniques for HCI were identified with the aim of selecting and evaluating an appropriate sensory technology for MCI. iii MCI technologies enable a computer system to recognise hand and other gestures of a user, creating means of direct interaction between a user and a computer as they are able to detect specific features extracted from a specific biological or physiological activity. Thereafter, Machine Learning (ML) is applied in order to train a computer system to detect these features and convert them to a computer interface. An application of biomedical signals (bio-signals) in HCI using a MYO Armband for MCI is presented. An MCI prototype (MCIp) was developed and implemented to allow a user to provide input to an HCI, in a hands-free and hands-full situation. The MCIp was designed and developed to recognise the hand-finger gestures of a person when both hands are free or when holding an object, such a cardboard box. The MCIp applies an Artificial Neural Network (ANN) to classify features extracted from the surface Electromyography signals acquired by the MYO Armband around the forearm muscle. The MCIp provided the results of data classification for gesture recognition to an accuracy level of 34.87% with a hands-free situation. This was done by employing the ANN. The MCIp, furthermore, enabled users to provide numeric inputs to the MCIp system hands-full with an accuracy of 59.7% after a training session for each gesture of only 10 seconds. The results were obtained using eight participants. Similar experimentation with the MYO Armband has not been found to be reported in any literature at submission of this document. Based on this novel experimentation, the main contribution of this research study is a suggestion that the application of a MYO Armband, as a commercially available muscle-sensing device on the market, has the potential as an MCI to recognise the finger gestures hands-free and hands-full. An accurate MCI can increase the efficiency and effectiveness of an HCI tool when it is applied to different applications in a warehouse where noise and hands-full activities pose a challenge. Future work to improve its accuracy is proposed

A framework to measure human behaviour whilst reading

The brain is the most complex object in the known universe that gives a sense of being to humans and characterises human behaviour. Building models of brain functions is perhaps the most fascinating scientific challenge in the 21st century. Reading is a significant cognitive process in the human brain that plays a critical role in the vital process of learning and in performing some daily activities. The study of human behaviour during reading has been an area of interest for researchers in different fields of science. This thesis is based upon providing a novel framework, called ARSAT (Assisting Researchers in the Selection of Appropriate Technologies), that measures the behaviour of humans when reading text. The ARSAT framework aims at assisting researchers in the selection and application of appropriate technologies to measure the behaviour of a person who is reading text. The ARSAT framework will assist to researchers who investigate the reading process and find it difficult to select appropriate theories, metrics, data collection methods and data analytics techniques. The ARSAT framework enhances the ability of its users to select appropriate metrics indicating the effective factors on the characterisation of different aspects of human behaviour during the reading process. As will be shown in this research study, human behaviour is characterised by a complicated interplay of action, cognition and emotion. The ARSAT framework also facilitates selecting appropriate sensory technologies that can be used to monitor and collect data for the metrics. Moreover, this research study will introduce BehaveNet, a novel Deep Learning modelling approach, which can be used for training Deep Learning models of human behaviour from the sensory data collected. In this thesis, a comprehensive literature study is presented that was conducted to acquire adequate knowledge for designing the ARSAT framework. In order to identify the contributing factors that affect the reading process, an overview of some existing theories of the reading process is provided. Furthermore, a number of sensory technologies and techniques that can be applied to monitoring the changes in the metrics indicating the factors are also demonstrated. Only, the technologies that are commercially available on the market are recommended by the ARSAT framework. A variety of Machine Learning techniques were also investigated when designing the BehaveNet. The BehaveNet takes advantage of the complementarity of Convolutional Neural Networks, Long Short-Term Memory networks and Deep Neural Networks. The design of a Human Behaviour Monitoring System (HBMS), by utilising the ARSAT framework for recognising three attention-seeking activities of humans, is also presented in this research study. Reading printed text, as well as speaking out loudly and watching a programme on TV were proposed as activities that a person unintentionally may shift his/her attention from reading into distractions. Between sensory devices recommended by the ARSAT framework, the Muse headband which is an Electroencephalography (EEG) and head motion-sensing wearable device, was selected to track the forehead EEG and a person’s head movements. The EEG and 3-axes accelerometer data were recorded from eight participants when they read printed text, as well as the time they performed two other activities. An imbalanced dataset consisting over 1.2 million rows of noisy data was created and used to build a model of the activities (60% training and 20% validating data) and evaluating the model (20% of the data). The efficiency of the framework is demonstrated by comparing the performance of the models built by utilising the BehaveNet, with the models built by utilising a number of competing Deep Learning models for raw EEG and accelerometer data, that have attained state-of-the-art performance. The classification results are evaluated by some metrics including the classification accuracy, F1 score, confusion matrix, Receiver Operating Characteristic curve, and Area under Curve (AUC) score. By considering the results, the BehaveNet contributed to the body of knowledge as an approach for measuring human behaviour by using sensory devices. In comparison with the performance of the other models, the models built by utilising the BehaveNet, attained better performance when classifying data of two EEG channels (Accuracy = 95%; AUC=0.99; F1 = 0.95), data of a single EEG channel (Accuracy = 85%; AUC=0.96; F1 = 0.83), accelerometer data (Accuracy = 81%; AUC = 0.9; F1 = 0.76) and all of the data in the dataset (Accuracy = 97%; AUC = 0.99; F1 = 0.96). The dataset and the source code of this project are also published on the Internet to help the science community. The Muse headband is also shown to be an economical and standard wearable device that can be successfully used in behavioural research

A framework to measure human behaviour whilst reading

The brain is the most complex object in the known universe that gives a sense of being to humans and characterises human behaviour. Building models of brain functions is perhaps the most fascinating scientific challenge in the 21st century. Reading is a significant cognitive process in the human brain that plays a critical role in the vital process of learning and in performing some daily activities. The study of human behaviour during reading has been an area of interest for researchers in different fields of science. This thesis is based upon providing a novel framework, called ARSAT (Assisting Researchers in the Selection of Appropriate Technologies), that measures the behaviour of humans when reading text. The ARSAT framework aims at assisting researchers in the selection and application of appropriate technologies to measure the behaviour of a person who is reading text. The ARSAT framework will assist to researchers who investigate the reading process and find it difficult to select appropriate theories, metrics, data collection methods and data analytics techniques. The ARSAT framework enhances the ability of its users to select appropriate metrics indicating the effective factors on the characterisation of different aspects of human behaviour during the reading process. As will be shown in this research study, human behaviour is characterised by a complicated interplay of action, cognition and emotion. The ARSAT framework also facilitates selecting appropriate sensory technologies that can be used to monitor and collect data for the metrics. Moreover, this research study will introduce BehaveNet, a novel Deep Learning modelling approach, which can be used for training Deep Learning models of human behaviour from the sensory data collected. In this thesis, a comprehensive literature study is presented that was conducted to acquire adequate knowledge for designing the ARSAT framework. In order to identify the contributing factors that affect the reading process, an overview of some existing theories of the reading process is provided. Furthermore, a number of sensory technologies and techniques that can be applied to monitoring the changes in the metrics indicating the factors are also demonstrated. Only, the technologies that are commercially available on the market are recommended by the ARSAT framework. A variety of Machine Learning techniques were also investigated when designing the BehaveNet. The BehaveNet takes advantage of the complementarity of Convolutional Neural Networks, Long Short-Term Memory networks and Deep Neural Networks. The design of a Human Behaviour Monitoring System (HBMS), by utilising the ARSAT framework for recognising three attention-seeking activities of humans, is also presented in this research study. Reading printed text, as well as speaking out loudly and watching a programme on TV were proposed as activities that a person unintentionally may shift his/her attention from reading into distractions. Between sensory devices recommended by the ARSAT framework, the Muse headband which is an Electroencephalography (EEG) and head motion-sensing wearable device, was selected to track the forehead EEG and a person’s head movements. The EEG and 3-axes accelerometer data were recorded from eight participants when they read printed text, as well as the time they performed two other activities. An imbalanced dataset consisting over 1.2 million rows of noisy data was created and used to build a model of the activities (60% training and 20% validating data) and evaluating the model (20% of the data). The efficiency of the framework is demonstrated by comparing the performance of the models built by utilising the BehaveNet, with the models built by utilising a number of competing Deep Learning models for raw EEG and accelerometer data, that have attained state-of-the-art performance. The classification results are evaluated by some metrics including the classification accuracy, F1 score, confusion matrix, Receiver Operating Characteristic curve, and Area under Curve (AUC) score. By considering the results, the BehaveNet contributed to the body of knowledge as an approach for measuring human behaviour by using sensory devices. In comparison with the performance of the other models, the models built by utilising the BehaveNet, attained better performance when classifying data of two EEG channels (Accuracy = 95%; AUC=0.99; F1 = 0.95), data of a single EEG channel (Accuracy = 85%; AUC=0.96; F1 = 0.83), accelerometer data (Accuracy = 81%; AUC = 0.9; F1 = 0.76) and all of the data in the dataset (Accuracy = 97%; AUC = 0.99; F1 = 0.96). The dataset and the source code of this project are also published on the Internet to help the science community. The Muse headband is also shown to be an economical and standard wearable device that can be successfully used in behavioural research

Improving selection of markers in nutrition research: evaluation of the criteria proposed by the ILSI Europe Marker Validation Initiative

The conduct of high-quality nutrition research requires the selection of appropriate markers as outcomes, for example as indicators of food or nutrient intake, nutritional status, health status or disease risk. Such selection requires detailed knowledge of the markers, and consideration of the factors that may influence their measurement, other than the effects of nutritional change. A framework to guide selection of markers within nutrition research studies would be a valuable tool for researchers. A multidisciplinary Expert Group set out to test criteria designed to aid the evaluation of candidate markers for their usefulness in nutrition research and subsequently to develop a scoring system for markers. The proposed criteria were tested using thirteen markers selected from a broad range of nutrition research fields. The result of this testing was a modified list of criteria and a template for evaluating a potential marker against the criteria. Subsequently, a semi-quantitative system for scoring a marker and an associated template were developed. This system will enable the evaluation and comparison of different candidate markers within the same field of nutrition research in order to identify their relative usefulness. The ranking criteria of proven, strong, medium or low are likely to vary according to research setting, research field and the type of tool used to assess the marker and therefore the considerations for scoring need to be determined in a setting-, field- and tool-specific manner. A database of such markers, their interpretation and range of possible values would be valuable to nutrition researchers

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Improved risk stratification of patients with atrial fibrillation: an integrated GARFIELD-AF tool for the prediction of mortality, stroke and bleed in patients with and without anticoagulation.

OBJECTIVES: To provide an accurate, web-based tool for stratifying patients with atrial fibrillation to facilitate decisions on the potential benefits/risks of anticoagulation, based on mortality, stroke and bleeding risks. DESIGN: The new tool was developed, using stepwise regression, for all and then applied to lower risk patients. C-statistics were compared with CHA2DS2-VASc using 30-fold cross-validation to control for overfitting. External validation was undertaken in an independent dataset, Outcome Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF). PARTICIPANTS: Data from 39 898 patients enrolled in the prospective GARFIELD-AF registry provided the basis for deriving and validating an integrated risk tool to predict stroke risk, mortality and bleeding risk. RESULTS: The discriminatory value of the GARFIELD-AF risk model was superior to CHA2DS2-VASc for patients with or without anticoagulation. C-statistics (95% CI) for all-cause mortality, ischaemic stroke/systemic embolism and haemorrhagic stroke/major bleeding (treated patients) were: 0.77 (0.76 to 0.78), 0.69 (0.67 to 0.71) and 0.66 (0.62 to 0.69), respectively, for the GARFIELD-AF risk models, and 0.66 (0.64-0.67), 0.64 (0.61-0.66) and 0.64 (0.61-0.68), respectively, for CHA2DS2-VASc (or HAS-BLED for bleeding). In very low to low risk patients (CHA2DS2-VASc 0 or 1 (men) and 1 or 2 (women)), the CHA2DS2-VASc and HAS-BLED (for bleeding) scores offered weak discriminatory value for mortality, stroke/systemic embolism and major bleeding. C-statistics for the GARFIELD-AF risk tool were 0.69 (0.64 to 0.75), 0.65 (0.56 to 0.73) and 0.60 (0.47 to 0.73) for each end point, respectively, versus 0.50 (0.45 to 0.55), 0.59 (0.50 to 0.67) and 0.55 (0.53 to 0.56) for CHA2DS2-VASc (or HAS-BLED for bleeding). Upon validation in the ORBIT-AF population, C-statistics showed that the GARFIELD-AF risk tool was effective for predicting 1-year all-cause mortality using the full and simplified model for all-cause mortality: C-statistics 0.75 (0.73 to 0.77) and 0.75 (0.73 to 0.77), respectively, and for predicting for any stroke or systemic embolism over 1 year, C-statistics 0.68 (0.62 to 0.74). CONCLUSIONS: Performance of the GARFIELD-AF risk tool was superior to CHA2DS2-VASc in predicting stroke and mortality and superior to HAS-BLED for bleeding, overall and in lower risk patients. The GARFIELD-AF tool has the potential for incorporation in routine electronic systems, and for the first time, permits simultaneous evaluation of ischaemic stroke, mortality and bleeding risks. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier for GARFIELD-AF (NCT01090362) and for ORBIT-AF (NCT01165710)

Two-year outcomes of patients with newly diagnosed atrial fibrillation: results from GARFIELD-AF.

AIMS: The relationship between outcomes and time after diagnosis for patients with non-valvular atrial fibrillation (NVAF) is poorly defined, especially beyond the first year. METHODS AND RESULTS: GARFIELD-AF is an ongoing, global observational study of adults with newly diagnosed NVAF. Two-year outcomes of 17 162 patients prospectively enrolled in GARFIELD-AF were analysed in light of baseline characteristics, risk profiles for stroke/systemic embolism (SE), and antithrombotic therapy. The mean (standard deviation) age was 69.8 (11.4) years, 43.8% were women, and the mean CHA2DS2-VASc score was 3.3 (1.6); 60.8% of patients were prescribed anticoagulant therapy with/without antiplatelet (AP) therapy, 27.4% AP monotherapy, and 11.8% no antithrombotic therapy. At 2-year follow-up, all-cause mortality, stroke/SE, and major bleeding had occurred at a rate (95% confidence interval) of 3.83 (3.62; 4.05), 1.25 (1.13; 1.38), and 0.70 (0.62; 0.81) per 100 person-years, respectively. Rates for all three major events were highest during the first 4 months. Congestive heart failure, acute coronary syndromes, sudden/unwitnessed death, malignancy, respiratory failure, and infection/sepsis accounted for 65% of all known causes of death and strokes for <10%. Anticoagulant treatment was associated with a 35% lower risk of death. CONCLUSION: The most frequent of the three major outcome measures was death, whose most common causes are not known to be significantly influenced by anticoagulation. This suggests that a more comprehensive approach to the management of NVAF may be needed to improve outcome. This could include, in addition to anticoagulation, interventions targeting modifiable, cause-specific risk factors for death. CLINICAL TRIAL REGISTRATION: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Techno-economic application of modular air cooling units for deep level mining at Mponeng

Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2009.As mining explores greater depths beyond 4000m at Mponeng Mine the challenges of obtaining suitable working environments and complying with regulations becomes significant. These depths mean a considerable increase in the virgin rock temperature and the surrounding working environment. By law the mine is obligated to provide working temperatures not exceeding 32°C wet bulb. This then necessitates the need for an effective cooling medium that not only has a great efficiency but should also be sustainable in the current economical environment faced with power supply shortages. Currently the advantages of ice are explored on Mponeng Below 120 level (120L) project with great effect. Due to the energy in the latent heat of fusion process, savings arise from lower pumping costs and conventional refrigeration plants are already being phased out. When applied in closed loop (U - tube) water cooling systems, colder water temperatures can be obtained at the working face. Currently Mponeng Mine is using Conventional Cooling Cars (CWC) that are similar in design to radiators for extracting heat from the working areas. The system absorbs the heat at the cooling car and rejects it in the fridge plants. If water temperatures raise this system is ineffective and costly from the loss of production. A solution for this problem comes in the form of a modular Air Cooling Unit (ACU) based on heat pump technology that was designed for use as a localised cooling unit. It allows for high supply water temperatures and provides cooling in the region of 100 kW. The study focuses on simulating the ACU in the proposed closed loop system for the Carbon Leader (CL) project and evaluating it with conventional cooling methods. Five different configurations between the 500 kW conventional cooler and 300 kW ACU are looked at and economically evaluated for the total life cycle costing (LCC). The study indicates that in a closed loop system that uses ice as the cooling medium CWCs is the best economical option. However ACUs proved to be economically the best method for cooling in two cases. Firstly in development ends and secondly using the warm return water to do additional underground cooling between the settlers and hot return water pumps. Based on the results from the study Mponeng should look into installing ACUs between the settlers and hot water dams as this scenario had the best net present value cost of all the simulations done.Master