40 research outputs found
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A Study on Active/Passive Pneumatic Actuators for Assistive Systems
The need for intelligent assistive devices is growing. Due to advances in medicine, people are living longer and able to recover from severe neurological incidents, resulting in an increased population with neuromuscular weakness. In workplaces such as assembly lines, there is a high possibility of work-related fatigue or injury, such as when workers squat down or lift their arms during their work tasks. Assistive devices could help remedy loss of strength on their extremities as well as keep the work environment safe and productive, allowing these growing segments of the population in need of the devices to live more self-sufficient, productive, and higher-quality lives.In the design of assistive systems, an important design goal is prolonged operational time, which requires the minimum usage of energy. Energy consumption can be reduced by modifying the mechanical characteristics of assistive systems according to the dynamic characteristics of the human body, which vary considerably between tasks. This dissertation investigates 1) the design of actuators with adjustable mechanical impedance, 2) control strategies to search for, and adjust to, a suitable mechanical impedance for assistance and 3) sensing technologies for classifying the tasks in which the human engages.The first part of this dissertation characterizes a pneumatic variable stiffness actuator named an Active/Passive Pneumatic Actuator (AP2A). The actuator consists of an air cylinder and an array of solenoid valves. These valves and the corresponding switching algorithms tune the chamber pressures and make the AP2A function as a mechanical spring with desired stiffness. The actuator has a low mechanical impedance compared to geared motors, which enables it to achieve efficient interaction. Control strategies of an assistive system with the AP2A are discussed in the second part. This control framework utilizes the characteristics of the AP2A to provide assistance when necessary and to operate transparently (i.e., neither to assist nor to disturb the users) otherwise. Energy consumed by the AP2A and the assisted system is minimized by solving an optimal control problem. Finally, an estimator is introduced to detect assistive timing for the assistive system with the AP2A. This estimator utilizes physiological signals such as surface electromyogram and prior knowledge of a muscular model, classifying if the user is under the specified condition to be assisted by the AP2A. It demonstrates that the user's effort can be saved, also reducing the number of procedures to collect training data for the estimator before using assistive systems. The performance of the actuator, the controller, and the estimator proposed in this dissertation are verified through experiments.From the above, this dissertation contributes to developing the AP2A that provides assistance and saves energy usage of assistive systems by working as a mechanical spring with stiffness optimized for achieving effective interaction under specific conditions. This actuator supports assistive devices that can be deployed in the real world, properly assisting the users when needed
A Model of the Roles of Essential Kinases in the Induction and Expression of Late Long-Term Potentiation
The induction of late long-term potentiation (L-LTP) involves complex
interactions among second messenger cascades. To gain insights into these
interactions, a mathematical model was developed for L-LTP induction in the CA1
region of the hippocampus. The differential equation-based model represents
actions of protein kinase A (PKA), MAP kinase (MAPK), and CaM kinase II
(CAMKII) in the vicinity of the synapse, and activation of transcription by CaM
kinase IV (CAMKIV) and MAPK. L-LTP is represented by increases in a synaptic
weight. Simulations suggest that steep, supralinear stimulus-response
relationships between stimuli (elevations in [Ca2+]) and kinase activation are
essential for translating brief stimuli into long-lasting gene activation and
synaptic weight increases. Convergence of multiple kinase activities to induce
L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply
with the number of tetanic electrical stimuli. The model simulates tetanic,
theta-burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to
synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of
MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to
delineate mechanisms underlying L-LTP induction and expression. For example,
the cAMP antagonist RpcAMPs, which inhibits L-LTP induction, is predicted to
inhibit ERK activation. The model also appears useful to clarify similarities
and differences between hippocampal L-LTP and long-term synaptic strengthening
in other systems.Comment: Accepted to Biophysical Journal. Single PDF, 7 figs include
Molecular Constraints on Synaptic Tagging and Maintenance of Long-Term Potentiation: A Predictive Model
Protein synthesis-dependent, late long-term potentiation (LTP) and depression
(LTD) at glutamatergic hippocampal synapses are well characterized examples of
long-term synaptic plasticity. Persistent increased activity of the enzyme
protein kinase M (PKM) is thought essential for maintaining LTP. Additional
spatial and temporal features that govern LTP and LTD induction are embodied in
the synaptic tagging and capture (STC) and cross capture hypotheses. Only
synapses that have been "tagged" by an stimulus sufficient for LTP and learning
can "capture" PKM. A model was developed to simulate the dynamics of key
molecules required for LTP and LTD. The model concisely represents
relationships between tagging, capture, LTD, and LTP maintenance. The model
successfully simulated LTP maintained by persistent synaptic PKM, STC, LTD, and
cross capture, and makes testable predictions concerning the dynamics of PKM.
The maintenance of LTP, and consequently of at least some forms of long-term
memory, is predicted to require continual positive feedback in which PKM
enhances its own synthesis only at potentiated synapses. This feedback
underlies bistability in the activity of PKM. Second, cross capture requires
the induction of LTD to induce dendritic PKM synthesis, although this may
require tagging of a nearby synapse for LTP. The model also simulates the
effects of PKM inhibition, and makes additional predictions for the dynamics of
CaM kinases. Experiments testing the above predictions would significantly
advance the understanding of memory maintenance.Comment: v3. Minor text edits to reflect published versio
Recommended from our members
A Study on Active/Passive Pneumatic Actuators for Assistive Systems
The need for intelligent assistive devices is growing. Due to advances in medicine, people are living longer and able to recover from severe neurological incidents, resulting in an increased population with neuromuscular weakness. In workplaces such as assembly lines, there is a high possibility of work-related fatigue or injury, such as when workers squat down or lift their arms during their work tasks. Assistive devices could help remedy loss of strength on their extremities as well as keep the work environment safe and productive, allowing these growing segments of the population in need of the devices to live more self-sufficient, productive, and higher-quality lives.In the design of assistive systems, an important design goal is prolonged operational time, which requires the minimum usage of energy. Energy consumption can be reduced by modifying the mechanical characteristics of assistive systems according to the dynamic characteristics of the human body, which vary considerably between tasks. This dissertation investigates 1) the design of actuators with adjustable mechanical impedance, 2) control strategies to search for, and adjust to, a suitable mechanical impedance for assistance and 3) sensing technologies for classifying the tasks in which the human engages.The first part of this dissertation characterizes a pneumatic variable stiffness actuator named an Active/Passive Pneumatic Actuator (AP2A). The actuator consists of an air cylinder and an array of solenoid valves. These valves and the corresponding switching algorithms tune the chamber pressures and make the AP2A function as a mechanical spring with desired stiffness. The actuator has a low mechanical impedance compared to geared motors, which enables it to achieve efficient interaction. Control strategies of an assistive system with the AP2A are discussed in the second part. This control framework utilizes the characteristics of the AP2A to provide assistance when necessary and to operate transparently (i.e., neither to assist nor to disturb the users) otherwise. Energy consumed by the AP2A and the assisted system is minimized by solving an optimal control problem. Finally, an estimator is introduced to detect assistive timing for the assistive system with the AP2A. This estimator utilizes physiological signals such as surface electromyogram and prior knowledge of a muscular model, classifying if the user is under the specified condition to be assisted by the AP2A. It demonstrates that the user's effort can be saved, also reducing the number of procedures to collect training data for the estimator before using assistive systems. The performance of the actuator, the controller, and the estimator proposed in this dissertation are verified through experiments.From the above, this dissertation contributes to developing the AP2A that provides assistance and saves energy usage of assistive systems by working as a mechanical spring with stiffness optimized for achieving effective interaction under specific conditions. This actuator supports assistive devices that can be deployed in the real world, properly assisting the users when needed