Abstract: A system that infers and detects how focused a worker is on work tasks, based on physiological parameters collected using wearable and environmental sensors. The system can identify whether a worker is in a focused state of flow, the affective state in which the worker's mind is fully immersed and actively engaged in their task. The system can also identify whether the worker is anxious (where the task demands/difficulty is too high relative the worker's skills) or bored (where the task is too easy). Once the focus state is detected, the information can be used in various applications for optimizing the worker's tasks and work quality such as monitoring task mastery, interventions to improve worker focus (e.g., recommending breaks, reassigning tasks, clarifying goals), identifying potentially lower quality production lots in a manufacturing environment, measuring the effects of skills training, and personal informatics for improving a worker's own productivity.

Abstract: An information processing apparatus includes a processor configured to estimate a waveform of heartbeats by inputting a waveform of a measured pulse wave to a model constructed by mounting a pulse wave measurement device and a heartbeat measurement device to a test subject and calculating the relationship between respective waveforms output from the devices.

Abstract: Example implementations described herein are directed to an interface for a worker dispatch system configured to dispatch routes and schedules for workers on a manufacturing floor. Example implementations can involve a system configured to execute a lot ordering and route optimization for each of a plurality of workers; determine rack placement of the plurality of racks and total travel distance for each worker; generate a deployment plan for each worker from the lot ordering, the route optimization, the rack placement, and the total travel distance, the deployment plan involving a pick-up route and a schedule for each worker; provide the deployment plan to an interface configured to receive modifications to the deployment plan; and upon receiving instructions to deploy the deployment plan through the interface, provide the deployment plan to a device of each worker.

Abstract: Example implementations described herein are directed to systems and methods for skill assessment, such as hand washing compliance in hospitals, or assembling products in factories. Example implementations involve body part tracking (e.g., hands), skeleton tracking and deep neural networks to detect and recognize sub-tasks and to assess the skill on each sub-task. Furthermore, the order of the sub-tasks is checked for correctness. Beyond monitoring individual users, example implementations can be used for analyzing and improving workflow designs with multiple sub-tasks.

Abstract: A real-time end-to-end system for capturing ink strokes written with ordinary pen and paper using a commodity video camera is described. Compare to traditional camera-based approaches, which typically separate out the pen tip localization and pen up/down motion detection, described is a unified approach that integrates these two steps using a deep neural network. Furthermore, the described system does not require manual initialization to locate the pen tip. A preliminary evaluation demonstrates the effectiveness of the described system on handwriting recognition for English and Japanese phrases.

Abstract: Example implementations described herein are directed to an interface for a worker dispatch system configured to dispatch routes and schedules for workers on a manufacturing floor. Example implementations can involve a system configured to execute a lot ordering and route optimization for each of a plurality of workers; determine rack placement of the plurality of racks and total travel distance for each worker; generate a deployment plan for each worker from the lot ordering, the route optimization, the rack placement, and the total travel distance, the deployment plan involving a pick-up route and a schedule for each worker; provide the deployment plan to an interface configured to receive modifications to the deployment plan; and upon receiving instructions to deploy the deployment plan through the interface, provide the deployment plan to a device of each worker.

Abstract: Example implementations described herein are directed to systems and methods for skill assessment, such as hand washing compliance in hospitals, or assembling products in factories. Example implementations involve body part tracking (e.g., hands), skeleton tracking and deep neural networks to detect and recognize sub-tasks and to assess the skill on each sub-task. Furthermore, the order of the sub-tasks is checked for correctness. Beyond monitoring individual users, example implementations can be used for analyzing and improving workflow designs with multiple sub-tasks.

Abstract: A real-time end-to-end system for capturing ink strokes written with ordinary pen and paper using a commodity video camera is described. Compare to traditional camera-based approaches, which typically separate out the pen tip localization and pen up/down motion detection, described is a unified approach that integrates these two steps using a deep neural network. Furthermore, the described system does not require manual initialization to locate the pen tip. A preliminary evaluation demonstrates the effectiveness of the described system on handwriting recognition for English and Japanese phrases.

Abstract: An image-feature-value calculating unit extracts an image feature value of an image of a cell candidate area. An NRBC discriminating unit uses a pre-trained discriminator to identify whether or not a target cell is shown in the cell candidate area, on the basis of the image feature value of the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discrimination result display unit displays the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discriminator training unit trains the discriminator by using the image feature value of the image of the cell candidate area as a training sample on the basis of a user input about whether or not a target cell is shown in the cell candidate area.

Abstract: A system that infers and detects how focused a worker is on work tasks, based on physiological parameters collected using wearable and environmental sensors. The system can identify whether a worker is in a focused state of flow, the affective state in which the worker's mind is fully immersed and actively engaged in their task. The system can also identify whether the worker is anxious (where the task demands/difficulty is too high relative the worker's skills) or bored (where the task is too easy). Once the focus state is detected, the information can be used in various applications for optimizing the worker's tasks and work quality such as monitoring task mastery, interventions to improve worker focus (e.g., recommending breaks, reassigning tasks, clarifying goals), identifying potentially lower quality production lots in a manufacturing environment, measuring the effects of skills training, and personal informatics for improving a worker's own productivity.

Abstract: An image processing apparatus includes a number-of-target-cells estimating unit for estimating, on the basis of a feature of a target sample, the number of target cells included in the target sample, and a detection parameter setting unit for setting, on the basis of the estimated number of target cells, a detection parameter regarding a process of detecting target cells in a captured image of the target sample.

Abstract: An image processing apparatus includes a detection-difficulty determination unit and a detection-parameter setting unit. The detection-difficulty determination unit calculates a degree of detection difficulty indicating a degree of difficulty in detection of a target cell contained in a specimen, based on a test object condition of the specimen. The detection-parameter setting unit sets a detection parameter for the detection of the target cell from a captured image of the specimen, based on the degree of detection difficulty.

Abstract: A detection control device includes a detection target image acquiring unit that acquires an imaged image resulting from imaging a specimen that is to be subjected to the detection of the target cell, a processing target region setting unit that sets a plurality of processing target regions in the imaged image, a processing target region distributing unit that distributes, in accordance with a distribution amount of data to each of a plurality of processing devices that detect the target cell in an image region, the plurality of processing target regions to the plurality of processing devices, and a detection result acquiring unit that acquires, from each of the plurality of processing devices, a result of a process of detecting the target cell in the processing target regions distributed to the respective plurality of processing devices.

Abstract: A device comprises: a nucleus-candidate-region extracting section that extracts, from a captured image obtained by image-capturing a sample piece including a target cell having a nucleus, a nucleus candidate region corresponding to the nucleus; a basic-probability-information acquiring section that acquires, for each of a plurality of determination subject regions determined on the basis of the nucleus candidate region extracted by the nucleus-candidate-region extracting section, basic probability information indicating probability that an image in the determination subject region is an image of the target cell, on the basis of a feature amount of the image of the determination subject region; and a probability-information calculating section that calculates probability information indicating probability that an image in a display subject region corresponding to the nucleus candidate region is the image of the target cell, on the basis of the basic probability information acquired for each of the plurality of

Abstract: A character recognition apparatus includes a stroke extracting unit, a noise-candidate extracting unit, a generating unit, a unit, and a specifying unit. The stroke extracting unit extracts multiple strokes from a recognition target. The noise-candidate extracting unit extracts noise candidates from the strokes. The generating unit generates multiple recognition result candidates obtained by removing at least one of the noise candidates from the recognition target. The unit performs character recognition on the recognition result candidates and obtains recognition scores. The specifying unit uses the recognition scores to specify a most likely recognition result candidate from the recognition result candidates as a recognition result.

Abstract: A character recognition apparatus includes a stroke extracting unit, a noise-candidate extracting unit, a generating unit, a unit, and a specifying unit. The stroke extracting unit extracts multiple strokes from a recognition target. The noise-candidate extracting unit extracts noise candidates from the strokes. The generating unit generates multiple recognition result candidates obtained by removing at least one of the noise candidates from the recognition target. The unit performs character recognition on the recognition result candidates and obtains recognition scores. The specifying unit uses the recognition scores to specify a most likely recognition result candidate from the recognition result candidates as a recognition result.

Abstract: A detection control device includes a detection target image acquiring unit that acquires an imaged image resulting from imaging a specimen that is to be subjected to the detection of the target cell, a processing target region setting unit that sets a plurality of processing target regions in the imaged image, a processing target region distributing unit that distributes, in accordance with a distribution amount of data to each of a plurality of processing devices that detect the target cell in an image region, the plurality of processing target regions to the plurality of processing devices, and a detection result acquiring unit that acquires, from each of the plurality of processing devices, a result of a process of detecting the target cell in the processing target regions distributed to the respective plurality of processing devices.

Abstract: An image processing apparatus includes a number-of-target-cells estimating unit for estimating, on the basis of a feature of a target sample, the number of target cells included in the target sample, and a detection parameter setting unit for setting, on the basis of the estimated number of target cells, a detection parameter regarding a process of detecting target cells in a captured image of the target sample.

Abstract: An image processing apparatus includes a detection-difficulty determination unit and a detection-parameter setting unit. The detection-difficulty determination unit calculates a degree of detection difficulty indicating a degree of difficulty in detection of a target cell contained in a specimen, based on a test object condition of the specimen. The detection-parameter setting unit sets a detection parameter for the detection of the target cell from a captured image of the specimen, based on the degree of detection difficulty.

Abstract: A device comprises: a nucleus-candidate-region extracting section that extracts, from a captured image obtained by image-capturing a sample piece including a target cell having a nucleus, a nucleus candidate region corresponding to the nucleus; a basic-probability-information acquiring section that acquires, for each of a plurality of determination subject regions determined on the basis of the nucleus candidate region extracted by the nucleus-candidate-region extracting section, basic probability information indicating probability that an image in the determination subject region is an image of the target cell, on the basis of a feature amount of the image of the determination subject region; and a probability-information calculating section that calculates probability information indicating probability that an image in a display subject region corresponding to the nucleus candidate region is the image of the target cell, on the basis of the basic probability information acquired for each of the plurality of