Biometrics is the process of measuring and analyzing human characteristics to verify a given pers... more Biometrics is the process of measuring and analyzing human characteristics to verify a given person's identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variability. Recent advances in Deep Learning and a deeper understanding of EEG processing methods have led to the development of models that accurately discriminate unique individuals. However, it is still uncertain how much EEG data is required to train such models. This work aims at determining the minimal amount of training data required to develop a robust EEG-based biometric model (+95% and +99% testing accuracies) from a subject for a task-dependent task. This goal is achieved by performing and analyzing 11,780 combinations of training sizes, by employing various neural network-based learning techniques of increasing complexity, and fe...
Human mental workload is arguably the most invoked multidimensional construct in Human Factors an... more Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. ...
This paper presents a method to extract train driver taskload from downloads of on-train-data-rec... more This paper presents a method to extract train driver taskload from downloads of on-train-data-recorders (OTDR). OTDR are in widespread use for the purposes of condition monitoring of trains, but they may also have applications in operations monitoring and management. Evaluation of train driver workload is one such application. The paper describes the type of data held in OTDR recordings and how they can be transformed into driver actions throughout a journey. Example data from 16 commuter journeys are presented, which highlights the increased taskload during arrival at stations. Finally, the possibilities and limitations of the data are discussed.
Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making p... more Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare. Difficulties arise from DRL’s opaque decision-making, hindering efficiency and resource use, this issue is amplified with every advancement. While many seek to move from Experience Replay to A3C, the latter demands more resources. Despite efforts to improve Experience Replay selection strategies, there is a tendency to keep the capacity high. We investigate training a Deep Convolutional Q-learning agent across 20 Atari games intentionally reducing Experience Replay capacity from 1×106 to 5×102. We find that a reduction from 1×104 to 5×103 doesn’t significantly affect rewards, offering a practical path to resource-efficient DRL. To illuminate agent decisions and align them with game mechanics, we employ a novel method: visualizing E...
ABSTRACT Clinicians often work under high-pressure, because of emergency situations, high volume,... more ABSTRACT Clinicians often work under high-pressure, because of emergency situations, high volume, or speed required. Their cognitive state is constant flux and while using digital interfaces, their experience and judgement is likely influenced by their mental state. Experience in aviation using the NASA-TLX (Task Load Index) tool for assessing Human Mental Workload (HMW) can be usefully applied along with Nielsen usability heuristics for evaluating an Electronic Health Records (EHR). A pilot study was conducted to assess clinicians cognitive state and investigate how HMW imposed by the EHR influences its usability. Two wards demanding different levels of physical/mental stress for staff, both using the same EHR to document patients daily progress, were compared. Ward-1: 18 long-stay elderly patients with high dependency scores; Ward-2: 10 short-stay elderly patients with low dependency score Method: Questionnaires incorporating the NASA-TLX model and Neilsens design/usability principles were completed by a clinician in each scenario, following each use of the EHR. The NASA-TLX model measures mental, physical and temporal demands, effort, performance and frustration levels. Results: HMW influences usability for the same EHR interface. Towards the end of day, clinician performance in using the EHR drastically decreases. They need to work harder mentally to reach the same level of performance (high HMW). Pearson correlation for Nielsen/NASA-TLX is significant (Ward-1: r = -0.86; Ward-2: r = -0.930). Increments of HMWs correspond to moderate decrements in usability. This evidence suggests that an EHR design process should consider more the context of use and the cognitive workload of its clinicians.
Biometrics is the process of measuring and analyzing human characteristics to verify a given pers... more Biometrics is the process of measuring and analyzing human characteristics to verify a given person's identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variability. Recent advances in Deep Learning and a deeper understanding of EEG processing methods have led to the development of models that accurately discriminate unique individuals. However, it is still uncertain how much EEG data is required to train such models. This work aims at determining the minimal amount of training data required to develop a robust EEG-based biometric model (+95% and +99% testing accuracies) from a subject for a task-dependent task. This goal is achieved by performing and analyzing 11,780 combinations of training sizes, by employing various neural network-based learning techniques of increasing complexity, and fe...
Human mental workload is arguably the most invoked multidimensional construct in Human Factors an... more Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. ...
This paper presents a method to extract train driver taskload from downloads of on-train-data-rec... more This paper presents a method to extract train driver taskload from downloads of on-train-data-recorders (OTDR). OTDR are in widespread use for the purposes of condition monitoring of trains, but they may also have applications in operations monitoring and management. Evaluation of train driver workload is one such application. The paper describes the type of data held in OTDR recordings and how they can be transformed into driver actions throughout a journey. Example data from 16 commuter journeys are presented, which highlights the increased taskload during arrival at stations. Finally, the possibilities and limitations of the data are discussed.
Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making p... more Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare. Difficulties arise from DRL’s opaque decision-making, hindering efficiency and resource use, this issue is amplified with every advancement. While many seek to move from Experience Replay to A3C, the latter demands more resources. Despite efforts to improve Experience Replay selection strategies, there is a tendency to keep the capacity high. We investigate training a Deep Convolutional Q-learning agent across 20 Atari games intentionally reducing Experience Replay capacity from 1×106 to 5×102. We find that a reduction from 1×104 to 5×103 doesn’t significantly affect rewards, offering a practical path to resource-efficient DRL. To illuminate agent decisions and align them with game mechanics, we employ a novel method: visualizing E...
ABSTRACT Clinicians often work under high-pressure, because of emergency situations, high volume,... more ABSTRACT Clinicians often work under high-pressure, because of emergency situations, high volume, or speed required. Their cognitive state is constant flux and while using digital interfaces, their experience and judgement is likely influenced by their mental state. Experience in aviation using the NASA-TLX (Task Load Index) tool for assessing Human Mental Workload (HMW) can be usefully applied along with Nielsen usability heuristics for evaluating an Electronic Health Records (EHR). A pilot study was conducted to assess clinicians cognitive state and investigate how HMW imposed by the EHR influences its usability. Two wards demanding different levels of physical/mental stress for staff, both using the same EHR to document patients daily progress, were compared. Ward-1: 18 long-stay elderly patients with high dependency scores; Ward-2: 10 short-stay elderly patients with low dependency score Method: Questionnaires incorporating the NASA-TLX model and Neilsens design/usability principles were completed by a clinician in each scenario, following each use of the EHR. The NASA-TLX model measures mental, physical and temporal demands, effort, performance and frustration levels. Results: HMW influences usability for the same EHR interface. Towards the end of day, clinician performance in using the EHR drastically decreases. They need to work harder mentally to reach the same level of performance (high HMW). Pearson correlation for Nielsen/NASA-TLX is significant (Ward-1: r = -0.86; Ward-2: r = -0.930). Increments of HMWs correspond to moderate decrements in usability. This evidence suggests that an EHR design process should consider more the context of use and the cognitive workload of its clinicians.
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Papers by Luca Longo