To press a button, a finger must push down and pull up with the right force and timing. How the motor system succeeds in button-pressing, in spite of neural noise and lacking direct access to the mechanism of the button, is poorly understood. This paper investigates a unifying account based on neurome- chanics. Mechanics is used to model muscles controlling the finger that contacts the button. Neurocognitive principles are used to model how the motor system learns appropriate muscle activations over repeated strokes though relying on degraded sensory feedback. Neuromechanical simulations yield a rich set of predictions for kinematics, dynamics, and user performance and may aid in understanding and improving input devices. We present a computational implementation and evaluate predictions for common button types.
[CHI 2018] Impact Activation Improves Rapid Button PressingSunjun Kim
The activation point of a button is defined as the depth at which it invokes a make signal. Regular buttons are activated during the downward stroke, which occurs within the first 20 ms of a press. The remaining portion, which can be as long as 80 ms, has not been examined for button activation for reason of mechanical limitations. The paper presents a technique and empirical evidence for an activation technique called Impact Activation, where the button is activated at its maximal impact point. We argue that this technique is advantageous particularly in rapid, repetitive button pressing, which is common in gaming and music applications. We report on a study of rapid button pressing, wherein users’ timing accuracy improved significantly with use of Impact Activation. The technique can be implemented for modern push-buttons and capacitive sensors that generate a continuous signal.
Stress detection and relief using wearable physiological sensorsTELKOMNIKA JOURNAL
The aim of the paper was to present a concept and to develop a prototype in the form of a cap
which uses a combination of physiological sensors that work in concert to not only detect high stress levels
in a person during his daily routine and working env ironment, but also initiate immediate relief measures.
The parameters used to detect stress were compared with resting heart rate and brainwave activity to
determine whether the person wearing the cap is in a stressed condition. Stress alleviation was achieved
using Auditory Stimulation and a Scalp Massage. Early detection of stress and its immediate remedy or
reduction can play an important role in preventing mental health disorders. In order to make the product
cost effective, the concept of sensing optimum amount of data to trigger a remedial action was given more
importance than extensive data collection using large number of sensors. Integrating an IOT device will
further allow information to be recorded and transmitted to a caregiver/doctor to prescribe remedial action
and thus prevent the condition to take a pathological form or get complicated. The detailed analysis of the
collected data can help people identify the precipitating factors for stress and thus aims at reduction of
stress related illnesses.
Automated Quantitative Measures of Forelimb Function in Rats and MiceInsideScientific
During this webinar Drew Sloan, PhD and Seth Hays, PhD discuss automated forelimb tasks for both rats and mice and applications of the quantitative data collected.
These new procedures are advancing discovery in basic neuroscience and offering deeper understanding of motor control and developing therapies for disease models such as traumatic brain injury, spinal cord injury, and Parkinson’s disease.
Dr. Drew Sloan demonstrates typical training and testing protocols for using the Vulintus MotoTrak behavioral system, including Isometric pull, supination and lever press tasks.
Following, Dr. Seth Hays shares his research which has used MotoTrak to investigate neuroplasticity-enhancing therapies for motor dysfunction, specifically looking at vagus nerve stimulation (VNS) as a method to promote plasticity.
[CHI 2018] Impact Activation Improves Rapid Button PressingSunjun Kim
The activation point of a button is defined as the depth at which it invokes a make signal. Regular buttons are activated during the downward stroke, which occurs within the first 20 ms of a press. The remaining portion, which can be as long as 80 ms, has not been examined for button activation for reason of mechanical limitations. The paper presents a technique and empirical evidence for an activation technique called Impact Activation, where the button is activated at its maximal impact point. We argue that this technique is advantageous particularly in rapid, repetitive button pressing, which is common in gaming and music applications. We report on a study of rapid button pressing, wherein users’ timing accuracy improved significantly with use of Impact Activation. The technique can be implemented for modern push-buttons and capacitive sensors that generate a continuous signal.
Stress detection and relief using wearable physiological sensorsTELKOMNIKA JOURNAL
The aim of the paper was to present a concept and to develop a prototype in the form of a cap
which uses a combination of physiological sensors that work in concert to not only detect high stress levels
in a person during his daily routine and working env ironment, but also initiate immediate relief measures.
The parameters used to detect stress were compared with resting heart rate and brainwave activity to
determine whether the person wearing the cap is in a stressed condition. Stress alleviation was achieved
using Auditory Stimulation and a Scalp Massage. Early detection of stress and its immediate remedy or
reduction can play an important role in preventing mental health disorders. In order to make the product
cost effective, the concept of sensing optimum amount of data to trigger a remedial action was given more
importance than extensive data collection using large number of sensors. Integrating an IOT device will
further allow information to be recorded and transmitted to a caregiver/doctor to prescribe remedial action
and thus prevent the condition to take a pathological form or get complicated. The detailed analysis of the
collected data can help people identify the precipitating factors for stress and thus aims at reduction of
stress related illnesses.
Automated Quantitative Measures of Forelimb Function in Rats and MiceInsideScientific
During this webinar Drew Sloan, PhD and Seth Hays, PhD discuss automated forelimb tasks for both rats and mice and applications of the quantitative data collected.
These new procedures are advancing discovery in basic neuroscience and offering deeper understanding of motor control and developing therapies for disease models such as traumatic brain injury, spinal cord injury, and Parkinson’s disease.
Dr. Drew Sloan demonstrates typical training and testing protocols for using the Vulintus MotoTrak behavioral system, including Isometric pull, supination and lever press tasks.
Following, Dr. Seth Hays shares his research which has used MotoTrak to investigate neuroplasticity-enhancing therapies for motor dysfunction, specifically looking at vagus nerve stimulation (VNS) as a method to promote plasticity.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Non-Contact Health Monitoring System Using Image and Signal ProcessingAtul Kumar Sharma
Presently digital medical devices promise to transform the future of medicine because of their ability to produce exquisitely detailed individual physiological data. As ordinary people start to have access and control over their own physiological data so that they can play a more active role in the management of their health. Currently many techniques are available for counting our heartbeat but it all needs bundles of sensors and wires. For heartbeat measurement using Electrocardiograph(ECG) method, we have to attach a bundle of leads in our chest and have to use adhesive gel. It is very difficult to patients and it can cause irritation to the skin. Another type is pulse oximeters and sensors, in this method sensors are attached to the finger tips or earlobes. This is also difficult for user.
In case of "Non-contact health monitoring system using image and signal processing" which gives contact free measurement about our physiological information using basic image processing devices. Users have the experience of real time health monitoring by just looking into "medical mirror". It recognizes our heartbeat without any external or internal sensor and displays it in real time. This invention helps people to access their own physiological data.
Work-related neck and shoulder pains are highly prevalent in jobs with low physical exposure. Myalgia of the trapezius muscle is one of the most prevalent work-related neck-shoulder disorders and muscle fatigue is widely considered a precursor of such disorders. There is evidence that long-lasting low-level activity of the trapezius muscle appears as a crucial link in the pathway from workplace physiological and psychological
demands to the development of work related neck pain. A possible approach to reduce the risks associated with muscle fatigue is to disrupt the monotonous muscle activity by adding frequent, active breaks during the working task. In the first phase of our investigation the long lasting component of trapezius muscle fatigue resulting from low level, sustained working task and spatio-temporal distribution of EMG activity are
investigated in two conditions including passive break or active disruption of muscle contraction.
Muscle fatigue develops and persists after the end of the workday. It appears that the alteration of force control may be associated with the corresponding fatigue. However, these phenomena seem to be counteracted by disruption of muscle contraction monotony by active interventions during the workday. Indeed, the presence of active disruptions also induces changes in the timing and degree of EMG activity as well as features of trapezius active areas. The extent of these adaptations appears to be subject and work task dependent but seem to be beneficial for the reduction of muscle fatigue.
Full Body Spatial Vibrotactile Brain Computer Interface ParadigmTakumi Kodama
T. Kodama, “Full Body Spatial Vibrotactile Brain Computer Interface Paradigm,” Master’s Thesis Defense, Department of Computer Science - Graduate School of Systems and Information Engineering, University of Tsukuba, Jan. 2017.
Recognition of new gestures using myo armband for myoelectric prosthetic appl...IJECEIAES
Myoelectric prostheses are a viable solution for people with amputations. The chal- lenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo armband device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.
Tactile Brain-Computer Interface Using Classification of P300 Responses Evoke...Takumi Kodama
Kodama T, Makino S, Rutkowski TM. Tactile Brain-Computer Interface Using Classification of P300 Responses Evoked by Full Body Spatial Vibrotactile Stimuli. In: Asia-Pacific Signal and Information Processing Association, 2016 Annual Summit and Conference (APSIPA ASC 2016). APSIPA. Jeju, Korea: IEEE Press; 2016.
This paper details the use of Electroencephalography, a methodology commonly applied for
medical purposes such as in detection of mental disorders and in upcoming technological research areas like BCI
(Brain Computer Interfaces), is now re-purposed to use in the Manufacturing sector to reduce the risk of error
and anomalies. Manufacturing involves many tasks that require mental alertness of an operator who supervises a
particular process, failure to do this, might leave unchecked errors in the finished product. Fatigue could lead to
serious consequences to health of the worker and may also lead to on-job accidents. To minimize possibility of
such instances, a study has been conducted to measure and find ways to tackle issues of mental fatigue. To
quantify the study, we have taken the case study of Pharmaceutical Sector where this kind of study might have
some impact. [3] The study reveals that workers doings tasks that require high alertness develop fatigue earlier
than anticipated, and therefore need frequent rotation from such activities.
This paper details the use of Electroencephalography, a methodology commonly applied for
medical purposes such as in detection of mental disorders and in upcoming technological research areas like BCI
(Brain Computer Interfaces), is now re-purposed to use in the Manufacturing sector to reduce the risk of error
and anomalies. Manufacturing involves many tasks that require mental alertness of an operator who supervises a
particular process, failure to do this, might leave unchecked errors in the finished product. Fatigue could lead to
serious consequences to health of the worker and may also lead to on-job accidents. To minimize possibility of
such instances, a study has been conducted to measure and find ways to tackle issues of mental fatigue. To
quantify the study, we have taken the case study of Pharmaceutical Sector where this kind of study might have
some impact. [3] The study reveals that workers doings tasks that require high alertness develop fatigue earlier
than anticipated, and therefore need frequent rotation from such activities.
Observations on typing from 136 million keystrokes - Presentation by Antti Ou...Aalto University
A CHI 2018 presentation by Antti Oulasvirta. "We report on typing behaviour and performance of 168,000 volunteers in an online study. The large dataset allows de- tailed statistical analyses of keystroking patterns, linking them to typing performance. Besides reporting distributions and confirming some earlier findings, we report two new findings. First, letter pairs typed by different hands or fingers are more predictive of typing speed than, for example, letter repetitions. Second, rollover-typing, wherein the next key is pressed before the previous one is released, is surprisingly prevalent. Notwith- standing considerable variation in typing patterns, unsuper- vised clustering using normalised inter-key intervals reveals that most users can be divided into eight groups of typists that differ in performance, accuracy, hand and finger usage, and rollover. The code and dataset are released for scientific use."
Project homepage with the dataset: http://userinterfaces.aalto.fi/136Mkeystrokes/
More Related Content
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Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Non-Contact Health Monitoring System Using Image and Signal ProcessingAtul Kumar Sharma
Presently digital medical devices promise to transform the future of medicine because of their ability to produce exquisitely detailed individual physiological data. As ordinary people start to have access and control over their own physiological data so that they can play a more active role in the management of their health. Currently many techniques are available for counting our heartbeat but it all needs bundles of sensors and wires. For heartbeat measurement using Electrocardiograph(ECG) method, we have to attach a bundle of leads in our chest and have to use adhesive gel. It is very difficult to patients and it can cause irritation to the skin. Another type is pulse oximeters and sensors, in this method sensors are attached to the finger tips or earlobes. This is also difficult for user.
In case of "Non-contact health monitoring system using image and signal processing" which gives contact free measurement about our physiological information using basic image processing devices. Users have the experience of real time health monitoring by just looking into "medical mirror". It recognizes our heartbeat without any external or internal sensor and displays it in real time. This invention helps people to access their own physiological data.
Work-related neck and shoulder pains are highly prevalent in jobs with low physical exposure. Myalgia of the trapezius muscle is one of the most prevalent work-related neck-shoulder disorders and muscle fatigue is widely considered a precursor of such disorders. There is evidence that long-lasting low-level activity of the trapezius muscle appears as a crucial link in the pathway from workplace physiological and psychological
demands to the development of work related neck pain. A possible approach to reduce the risks associated with muscle fatigue is to disrupt the monotonous muscle activity by adding frequent, active breaks during the working task. In the first phase of our investigation the long lasting component of trapezius muscle fatigue resulting from low level, sustained working task and spatio-temporal distribution of EMG activity are
investigated in two conditions including passive break or active disruption of muscle contraction.
Muscle fatigue develops and persists after the end of the workday. It appears that the alteration of force control may be associated with the corresponding fatigue. However, these phenomena seem to be counteracted by disruption of muscle contraction monotony by active interventions during the workday. Indeed, the presence of active disruptions also induces changes in the timing and degree of EMG activity as well as features of trapezius active areas. The extent of these adaptations appears to be subject and work task dependent but seem to be beneficial for the reduction of muscle fatigue.
Full Body Spatial Vibrotactile Brain Computer Interface ParadigmTakumi Kodama
T. Kodama, “Full Body Spatial Vibrotactile Brain Computer Interface Paradigm,” Master’s Thesis Defense, Department of Computer Science - Graduate School of Systems and Information Engineering, University of Tsukuba, Jan. 2017.
Recognition of new gestures using myo armband for myoelectric prosthetic appl...IJECEIAES
Myoelectric prostheses are a viable solution for people with amputations. The chal- lenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo armband device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.
Tactile Brain-Computer Interface Using Classification of P300 Responses Evoke...Takumi Kodama
Kodama T, Makino S, Rutkowski TM. Tactile Brain-Computer Interface Using Classification of P300 Responses Evoked by Full Body Spatial Vibrotactile Stimuli. In: Asia-Pacific Signal and Information Processing Association, 2016 Annual Summit and Conference (APSIPA ASC 2016). APSIPA. Jeju, Korea: IEEE Press; 2016.
This paper details the use of Electroencephalography, a methodology commonly applied for
medical purposes such as in detection of mental disorders and in upcoming technological research areas like BCI
(Brain Computer Interfaces), is now re-purposed to use in the Manufacturing sector to reduce the risk of error
and anomalies. Manufacturing involves many tasks that require mental alertness of an operator who supervises a
particular process, failure to do this, might leave unchecked errors in the finished product. Fatigue could lead to
serious consequences to health of the worker and may also lead to on-job accidents. To minimize possibility of
such instances, a study has been conducted to measure and find ways to tackle issues of mental fatigue. To
quantify the study, we have taken the case study of Pharmaceutical Sector where this kind of study might have
some impact. [3] The study reveals that workers doings tasks that require high alertness develop fatigue earlier
than anticipated, and therefore need frequent rotation from such activities.
This paper details the use of Electroencephalography, a methodology commonly applied for
medical purposes such as in detection of mental disorders and in upcoming technological research areas like BCI
(Brain Computer Interfaces), is now re-purposed to use in the Manufacturing sector to reduce the risk of error
and anomalies. Manufacturing involves many tasks that require mental alertness of an operator who supervises a
particular process, failure to do this, might leave unchecked errors in the finished product. Fatigue could lead to
serious consequences to health of the worker and may also lead to on-job accidents. To minimize possibility of
such instances, a study has been conducted to measure and find ways to tackle issues of mental fatigue. To
quantify the study, we have taken the case study of Pharmaceutical Sector where this kind of study might have
some impact. [3] The study reveals that workers doings tasks that require high alertness develop fatigue earlier
than anticipated, and therefore need frequent rotation from such activities.
Observations on typing from 136 million keystrokes - Presentation by Antti Ou...Aalto University
A CHI 2018 presentation by Antti Oulasvirta. "We report on typing behaviour and performance of 168,000 volunteers in an online study. The large dataset allows de- tailed statistical analyses of keystroking patterns, linking them to typing performance. Besides reporting distributions and confirming some earlier findings, we report two new findings. First, letter pairs typed by different hands or fingers are more predictive of typing speed than, for example, letter repetitions. Second, rollover-typing, wherein the next key is pressed before the previous one is released, is surprisingly prevalent. Notwith- standing considerable variation in typing patterns, unsuper- vised clustering using normalised inter-key intervals reveals that most users can be divided into eight groups of typists that differ in performance, accuracy, hand and finger usage, and rollover. The code and dataset are released for scientific use."
Project homepage with the dataset: http://userinterfaces.aalto.fi/136Mkeystrokes/
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Talk by Andreas Karrenbauer / Max Planck Institute for Informatics. Presented at the CHI conference (chi2018.acm.org) by Andreas Karrenbauer / Max Planck. In collaboration with Anna Maria Feit, Antti Oulasvita, and Perttu Lähteenlahti
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HCI Research as Problem-Solving [CHI'16, presentation slides] Aalto University
Slides from a talk delivered at CHI 2016, San Jose.
Authors: Antti Oulasvirta (Aalto University) and Kasper Hornbaek (University of Copenhagen).
Link to paper: http://users.comnet.aalto.fi/oulasvir/pubs/hci-research-as-problem-solving-chi2016.pdf
Overview: This talk discusses a meta-scientific account of human-computer interaction (HCI) research as problem-solving. We build on the philosophy of Larry Laudan, who develops problem and solution as the foundational concepts of science. We argue that most HCI research is about three main types of problem: empirical, conceptual, and constructive. We elaborate upon Laudan’s concept of problem-solving capacity as a universal criterion for determining the progress of solutions (outcomes): Instead of asking whether research is ‘valid’ or follows the ‘right’ approach, it urges us to ask how its solutions advance our capacity to solve important problems in human use of computers. This offers a rich, generative, and ‘discipline-free’ view of HCI and resolves some existing debates about what HCI is or should be. It may also help unify efforts across nominally disparate traditions in empirical research, theory, design, and engineering.
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The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
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https://www.etran.rs/2024/en/home-english/
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As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
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the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
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during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
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Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
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Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
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Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
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Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Neuromechanics of a Button Press: A talk at CHI 2018, April 2018
1. Neuromechanics
Antti Oulasvirta, Sunjun Kim, and Byungjoo Lee bit.do/neuromechanics
of a Button Press
Related papers at CHI 2018:
1. Control-theoretic Models of Pointing Tue 9-11.30 517C
2. Impact Activation Improves Rapid Key Pressing Mon 16.30-17.50 514AB
3. Moving Target Selection: A Cue Integration Model This session
2. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics10x slowdown
Long-term goal:
A theory of input
Design
Feedback
Goals
Anatomy
Skill
Movement
Efficiency
Efficacy
Effort
3. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Existing theories
Information theory
Human performance
Control theory
Cognitive models
Characterize
Intrapolate
Poor transfer
Change in device or task
insists on data collection
or manual task modeling
4. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
The brain’s point-of-view
5. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Design
Anatomy
Goals
Feedback
Kinematics
Dynamics
Precision
Effort
Human-like
responses
Adapt
A generative
approach
6. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
The
physical
The
neural
What is neuromechanics?
Neural principles of motor control in biomechanical systems
The
physiological
[Enoka 2009]
7. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Input is control of mediated sensations
The
brain
Peripheral
nervous system
Sensory
nervous system
Action
potential
Physical
stimulation
Feedforward
Feedback
Limbs
Sensory organs
Device
8. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Probabilistic
internal model
Sensory
signals
Activation
signal
Cue
integration
Integrated
percept
Perceptual
control task
Prediction
error
Prediction
Muscle, bone,
tissue, device,
sensory organs
Overview of the theory
9. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Theory: Elements
Biomechanical simulation
Cue integration
Probabilistic internal model
Perceptual control
[Enoka 2009]
[Sung-Hee Lee 2009]
[Ernst 2004, 2006]
[Powers 1973, 2009]
[Clark 2013]
[Hohwy 2016]
10. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Too brief for closed-loop control
Button mechanism not perceived
Ephemeral sensations
Noisy neuromuscular system
10x slowdown
Precise time and force
Effective skill transfer
Ability to adapt and recover
Button-
pressing:
A miracle
11. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Random
movements are
unsuccessful
12. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Biomechanics
simulation
Noisy muscle
activation signal
Mechanoreceptive
sensor
Proprioceptive
sensor
13. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Silicone
finger tip
Pressure
sensor
Hill type
muscle
Joint angle
sensor
Real button
Robotic
implementation
+ Noise
+ Noise+ Noise
14. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
No motor noise
à super-human performance
15. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Sensory signals
Cue integration
Perceptual center
eeds a threshold value. This
ls: visual and auditory (beep).
o-beep delays are assumed to
utation of p-Centers
m is connected to four extero-
ion, proprioception, audition,
ity i produces a p-center pci.
sfer of a neural signal evoked
hanoreceptors. We are espe-
noreceptors on the finger pad
ming of a button press. Slowly
ensitive to coarse spatial struc-
flat top surface of the button),
ers respond to motion. Kim
in signals from the fingertip
ion, and jerk from the finger
t force and indentation have
d force correlates highly with
use maximum likelihood estimation (MLE) to obtain
estimate of pco. For another implementation of cue inte
see [35]. In MLE, assuming that a single-cue estim
unbiased but corrupted by Gaussian noise, the optimal s
for estimating pco is a weighted average [16, 17]:
pco = Â
i
wi pci where wi =
1/s2
i
Âi 1/s2
i
with wi being the weight given to the ith single-cue es
and s2
i being that estimate’s variance. Figure 6 sho
emplary p-center calculations: signal-specific (pci) an
grated p-centers (pco) from 100 simulated runs of NEU
CHANIC pressing a tactile button. Note that absolute
ences among pci do not affect pco, only signal varian
The integrated timing estimate is robust to long delays
auditory or visual feedback. This assumption is base
study showing that physiological events that take place q
within a few hundred milliseconds, do not tend to be
over- nor underestimations of event durations [14].
Maximum likelihood estimator
“When was the
button activated?”
Modality-specific
noise variances
Proprio-
ceptive
Tactile
Integration is
sensitive to how
reliable the cues are
16. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
No mechanoreceptive
feedback signal
17. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Perceptual control task
Choose (1) a motor command and (2) expected perception that
minimizes this objective:
The
oned
ntin-
prio-
ion),
Gaus-
form
error
tiva-
cuss
esult
ptual
enter
com-
f cue
(GP)
and
e the
with signal offset µ, signal amplitude t, and duration s of the
agonist (A+) muscle. We have set physiologically plausible
extrema (min and max) for the activation parameters. Note
that this formulation assumes that the antagonist muscle re-
sists motion passively. More determinate pull-up motion can
be achieved by adding similar parameters for the antagonist
muscle (A-).
The objective is to determine motor command (q) and as-
sociated estimate of perceived button activation (pce) that
minimize error:
min
q,pce
EP(q, pce)+EA(q)+EC(q)+wFM(q) (2)
where EP is perceptual error, EA is error in activating the
button, and EC is error in making contact (button cap not
touched). FM is muscle force expenditure computed from the
Hill muscle model (see below), and w is a tuning factor. We
assume that activation and contact errors are trivial to perceive.
Therefore, EA and EC are binary: 1 in the case of error and 0
otherwise. Perceptual error EP is defined as distance (in time)
between expected p-center pce and observed p-center pco:
Objective function
Perceptual
error
Activation
failure
(binary)
Contact
failure
(binary)
Muscle
force
User goal:
Lightness
vs.
precision
18. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Perceptual error
Minimize error between expected and perceived activation time
19. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
No button-
activation term
20. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
No muscle
effort term
21. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
A probabilistic internal model
learns motor outputs that minimize the perceptual objective
A Bayesian optimizer with a Gaussian Process prior
Approximate Bayesian Computation (ABC)Approximate Bayesian Computation (ABC) Approximate Bayesian Computat
...
Finds a good button
press after 10-20 trials
22. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Model
parameters
Table 1. Model parameters. Button parameters here given for physical
buttons. Task parameters (e.g., finger starting height) are given in text.
f denotes function
Variable Description Value, Unit Ref.
fr Radius of finger cone 7.0 mm
fw Length of finger 60 mm
rf Density of finger 985 kg/m3
cf Damping of finger pulp 1.5 N·s/m [64]
kf Stiffness of finger pulp f, N/m [65]
wb Width of key cap 14 mm
db Depth of key cap 10 mm
rb Density of key cap 700 kg/m3
cb Damping of button 0.1 N·s/m
ks Elasticity of muscle 0.8·PCSA [38]
kd Elasticity of muscle 0.1·ks [38]
kc Damping of muscle 6 N·s/m [38]
PCSA Phys. cross-sectional area 4 cm2
L0ag, L0an Initial muscle length 300 mm
sn Neuromuscular noise 5·10 2
sm Mechanoreception noise 1·10 8
sp Proprioception noise 8·10 7
sa Sound and audition noise 5·10 4
sv Display and vision noise 2·10 2
system. We have set these parameters manually in order to
reproduce certain basic effects: Neuromuscular noise, which
reflects the joint additive contribution of neural and muscular
Physically
measurable
Tuned based on
literature
23. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Simulation workbench (MATLAB)
bit.do/neuromechanics
But are the
outputs
realistic?
24. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Precision & success: Predictions
Figure 7. Data collection on press kinematics: A single-subject study.
High-fidelity optical motion tracking was used to track a marker on
the finger nail. A custom-made single-button setup was created using
switches and key caps from commercial keyboards.
SIMULATIONS: COMPARING BUTTON DESIGNS
Most precise Less precise Least precise
High success High success Low success
Push-
button
Touch Mid-air
Order supported by literature
25. Data collection: A single-subject study
“Press rhythmically in a
manner natural for you”
26. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Mid-air button: Kinematics
[Torre & Balasubramaniam 2009]
Human Model
Similar to our data and literature
27. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Dynamics
Force-displacement curves
Predicted
peak forces
1.5-2.9N
Force ranges similar
to literature
cle force–displacement behavior for a tactile
with an effort-minimizing term in the objective
task performance (performance in button activation). We co
clude, that although much work remains to be done, the resul
support the ’optimal black box’ assumption. And many mor
analyses could done, such as looking at the effect of unreliab
feedback, oscillation of the finger tip, such as when walkin
or the effects that impairments like essential tremor have.
FUTURE WORK
Modeling latent neural and cognitive constructs, such as nois
poses a scientific challenge for future research. Change i
noise parameters has a large and poorly understood effect o
dynamics downstream. However, without noise, a button ca
be activated with arbitrary precision. For example, cuttin
sensory noise parameters to 10 9 reduces perceptual error t
the order of 1.5·10 6 s. Our noise model was tuned manuall
to reproduce some standard findings on sensory modalities. T
“Light touch”
Force(N)
28. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Touch buttons: Kinematics
Displacement-velocity curves
e 8. Displacement–velocity curves for four button types from single-subject recordings (top) and simulations (bottom
Shape similar except in release
Human Model
Similar result for push-buttons
29. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Signal-dependent
noise
Muscle and
joint models
Force
perception
Noise parameter
identification
Limitations
30. Neuromechanics of a Button Press
Oulasvirta, Kim, & Lee Proc. CHI 2018 bit.do/neuromechanics
Neuro-
science PhysicsPhysiology
Machine
learning
Biomech.
simulation
EE and signal
processing
A unifying account
A generative simulation
Can it be made to work beyond buttons?
31. bit.do/neuromechanics
Antti Oulasvirta, Sunjun Kim, Byungjoo Lee
Acknowledgements: Jong-In Lee, Aleksi
Pesonen, Yunfei Xiu, and Crista Kaukinen
Related papers at CHI 2018:
1. Control-theoretic models of Pointing Tue 9.00 517C
2. Impact Activation Improves Rapid Key Pressing Mon 16.30 514AB
3. Moving Target Selection: A Cue Integration Model This session
Figure 8. Displacement–velocity curves for four button types from single-subject recordings (top) and simulations (bottom).
Data Matlab codeRobot