scholarly journals Individualized tracking of self-directed motor learning in group-housed mice performing a skilled lever positioning task in the home cage

2018 ◽  
Vol 119 (1) ◽  
pp. 337-346 ◽  
Author(s):  
Gergely Silasi ◽  
Jamie D. Boyd ◽  
Federico Bolanos ◽  
Jeff M. LeDue ◽  
Stephen H. Scott ◽  
...  
Keyword(s):  
Motor Learning ◽  
Radio Frequency Identification ◽  
Home Cage ◽  
Low Cost ◽  
Motor Task ◽  
Training Group ◽  
Cortical Reorganization ◽  
Raspberry Pi ◽  
Internet Connection ◽  
Cost System

Skilled forelimb function in mice is traditionally studied through behavioral paradigms that require extensive training by investigators and are limited by the number of trials individual animals are able to perform within a supervised session. We developed a skilled lever positioning task that mice can perform within their home cage. The task requires mice to use their forelimb to precisely hold a lever mounted on a rotary encoder within a rewarded position to dispense a water reward. A Raspberry Pi microcomputer is used to record lever position during trials and to control task parameters, thus making this low-footprint apparatus ideal for use within animal housing facilities. Custom Python software automatically increments task difficulty by requiring a longer hold duration, or a more accurate hold position, to dispense a reward. The performance of individual animals within group-housed mice is tracked through radio-frequency identification implants, and data stored on the microcomputer may be accessed remotely through an active internet connection. Mice continuously engage in the task for over 2.5 mo and perform ~500 trials/24 h. Mice required ~15,000 trials to learn to hold the lever within a 10° range for 1.5 s and were able to further refine movement accuracy by limiting their error to a 5° range within each trial. These results demonstrate the feasibility of autonomously training group-housed mice on a forelimb motor task. This paradigm may be used in the future to assess functional recovery after injury or cortical reorganization induced by self-directed motor learning. NEW & NOTEWORTHY We developed a low-cost system for fully autonomous training of group-housed mice on a forelimb motor task. We demonstrate the feasibility of tracking both end-point, as well as kinematic performance of individual mice, with each performing thousands of trials over 2.5 mo. The task is run and controlled by a Raspberry Pi microcomputer, which allows for cages to be monitored remotely through an active internet connection.

scholarly journals Low Cost System for Laboratory-based Course in IT Education using Raspberry Pi

2020 ◽  
Author(s):  
Tae-Hoon Kim ◽  
Ricardo Calix ◽  
Dhruvkumar Patel
Keyword(s):  
Low Cost ◽  
Raspberry Pi ◽  
Cost System ◽  
It Education

scholarly journals Development of Smart Vehicle Security and Entertainment System (SSES) using Raspberry Pi

2020 ◽  
Vol 9 (3) ◽  
pp. 4077-4083 ◽  
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Security System ◽  
Local Network ◽  
Raspberry Pi ◽  
Main Concept ◽  
Embedded Devices ◽  
Vehicle Owner ◽  
Cost System ◽  
Vehicle Security

This paper deals with development of a Vehicle Security and Entertainment System, which is being used to monitor, track the vehicle, and to offer local entertainment system. The development system makes used of two embedded devices to split the entertainment system from the security system to ensure isolation and security. The security system is equipped with camera, distress signal switch and GPS/GPRS module to track, report a problem, and monitor the vehicle by sending data to a centralized database server where vehicle owner can access and retrieve these data to guarantee the safety of the passengers and the vehicle too. The second system is the entertainment system, where this system uses a powerful Intel atom embedded device and local network to allow users to connect and offer entertaining services. These services include, E-Book library and multimedia streaming. The main concept of research to develop a low cost system to secure and entertain passengers on vehicles like buses, train and even cars. The development is cost effective and as well as can be modified to add extra modules or to develop extra entertainment services. If the vehicle is stolen the system is able to send a distress signal to the owner or company. They can help the passengers by monitoring through the vehicle camera. In this research we have successfully developed and tested the system.

Gamma frequencies as a predictor for the accomplishment of a motor task guided through the action observation network

2020 ◽  
pp. 1-10
Author(s):  
Toledo Felippe ◽  
Thaler Markus
Keyword(s):  
Motor Learning ◽  
Motor Performance ◽  
Motor Behavior ◽  
Low Cost ◽  
Action Observation ◽  
Motor Task ◽  
Gamma Oscillations ◽  
Therapeutic Interventions ◽  
Action Observation Network ◽  
Observation Network

BACKGROUND: Action observation describes a concept where the subsequent motor behavior of an individual can be modulated though observing an action. This occurs through the activation of neurons in the action observation network, acting on a variety of motor learning processes. This network has been proven highly useful in the rehabilitation of patients with acquired brain injury, placing “action observation” as one of the most effective techniques for motor recovery in physical neurorehabilitation. OBJECTIVE: The aim of this paper is to define an EEG marker for motor learning, guided through observation. METHODS: Healthy subjects (n = 41) participated voluntarily for this research. They were asked to repeat an unknown motor behavior, immediately after observing a video. During the observation, EEG raw signals where collected with a portable EEG and the results were later compared with success and fail on repeating the motor procedure. The comparison was then analyzed with the Mann-Whitney U test for non-parametrical data, with a confidence interval of 95%. RESULTS: A significant relation between motor performance and neural activity was found for Alpha (p = 0,0149) and Gamma (0,0005) oscillatory patterns. CONCLUSION: Gamma oscillations with frequencies between 41 and 49,75 Hz, seem to be an adequate EEG marker for motor performance guided through the action observation network. The technology used for this paper is easy to use, low-cost and presents valid measurements for the recommended oscillatory frequencies, implying a possible use on rehabilitation, by collecting data in real-time during therapeutic interventions and assessments.

scholarly journals Low cost audiovisual playback and recording triggered by radio frequency identification using Raspberry Pi

2014 ◽  
Author(s):  
Adam Z Lendvai ◽  
Çağlar Akçay ◽  
Talia Weiss ◽  
Mark F. Haussmann ◽  
Ignacio T Moore ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Behavioral Ecology ◽  
Radio Frequency Identification ◽  
Visual Information ◽  
Low Cost ◽  
Raspberry Pi ◽  
Male Tree ◽  
Frequency Identification ◽  
Nestling Begging ◽  
Set Up

Carrying out playbacks of visual or audio stimuli to wild animals is a widely used experimental tool in behavioral ecology. In many cases, however, playback experiments are constrained by observer limitations such as the time observers can be present, or the accuracy of observation. These problems are particularly apparent when playbacks are triggered by specific events or are targeted to specific individuals. We developed a low-cost automated playback/recording system, using two field-deployable devices: radio-frequency identification (RFID) readers and Raspberry Pi micro-computers. This system detects a specific passive integrated transponder (PIT) tag attached to an individual, and subsequently plays back the stimuli, or records audio or visual information. To demonstrate the utility of this system, we tagged female and male tree swallows from two box-nesting populations with PIT tags and carried out playbacks of nestling begging calls every time females entered the nestbox over a six-hour period. We show that the RFID-Raspberry Pi system presents a versatile, low-cost, field-deployable system that can be adapted for many audio and visual playback purposes. The low cost and the small learning curve make this set-up a feasible system for use by field biologists.

scholarly journals Impaired refinement of kinematic variability in Huntington disease mice on an automated home-cage forelimb motor task

2021 ◽  
Author(s):  
Cameron L. Woodard ◽  
Marja D. Sepers ◽  
Lynn A. Raymond
Keyword(s):  
Motor Learning ◽  
Mouse Models ◽  
Huntington Disease ◽  
Neurological Disorders ◽  
Home Cage ◽  
Neurodegenerative Disorder ◽  
Motor Task ◽  
Testing System ◽  
Neuronal Dysfunction ◽  
Movement Variability

AbstractThe effective development of novel therapies in mouse models of neurological disorders relies on behavioural assessments that provide accurate read-outs of neuronal dysfunction and/or degeneration. We designed an automated behavioural testing system (‘PiPaw’) which integrates an operant lever-pulling task directly into the mouse home-cage. This task is accessible to group-housed mice 24-hours per day, enabling high-throughput longitudinal analysis of forelimb motor learning. Moreover, this design eliminates the need for exposure to novel environments and minimizes experimenter interaction, significantly reducing two of the largest stressors associated with animal behaviour. Mice improved their performance of this task over one week of testing by reducing inter-trial variability of reward-related kinematic parameters (pull amplitude or peak velocity). In addition, mice displayed short-term improvements in reward rate, and a concomitant decrease in movement variability, over the course of brief (<10 minutes) bouts of task engagement. We used this system to assess motor learning in mouse models of the inherited neurodegenerative disorder, Huntington disease (HD). Despite having no baseline differences in task performance, Q175-FDN HD mice were unable to modulate the variability of their movements in order to increase reward on either short or long timescales. Task training was associated with a decrease in the amplitude of spontaneous excitatory activity recorded from striatal medium spiny neurons in the hemisphere contralateral to the trained forelimb in wildtype mice; however, no such changes were observed in Q175-FDN mice. This behavioural screening platform should prove useful for preclinical drug trials towards improved treatments in HD and other neurological disorders.Significance StatementIn order to develop effective therapies for neurological disorders such as Huntington disease (HD), it’s important to be able to accurately and reliably assess the behaviour of mouse models of these conditions. Moreover, these behavioural assessments should provide an accurate readout of underlying neuronal dysfunction and/or degeneration. In this paper, we employed an automated behavioural testing system to assess motor learning in mice within their home-cage. Using this system, we were able to study motor abnormalities in HD mice with an unprecedented level of detail, and identified a specific behavioural deficit associated with an underlying impairment in striatal neuronal plasticity. These results validate the usefulness of this system for assessing behaviour in mouse models of HD and other neurological disorders.

scholarly journals Low cost audiovisual playback and recording triggered by radio frequency identification using Raspberry Pi

2014 ◽  
Author(s):  
Adam Z Lendvai ◽  
Çağlar Akçay ◽  
Talia Weiss ◽  
Mark F. Haussmann ◽  
Ignacio T Moore ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Behavioral Ecology ◽  
Radio Frequency Identification ◽  
Visual Information ◽  
Low Cost ◽  
Raspberry Pi ◽  
Male Tree ◽  
Frequency Identification ◽  
Nestling Begging ◽  
Set Up

Carrying out playbacks of visual or audio stimuli to wild animals is a widely used experimental tool in behavioral ecology. In many cases, however, playback experiments are constrained by observer limitations such as the time observers can be present, or the accuracy of observation. These problems are particularly apparent when playbacks are triggered by specific events or are targeted to specific individuals. We developed a low-cost automated playback/recording system, using two field-deployable devices: radio-frequency identification (RFID) readers and Raspberry Pi micro-computers. This system detects a specific passive integrated transponder (PIT) tag attached to an individual, and subsequently plays back the stimuli, or records audio or visual information. To demonstrate the utility of this system, we tagged female and male tree swallows from two box-nesting populations with PIT tags and carried out playbacks of nestling begging calls every time females entered the nestbox over a six-hour period. We show that the RFID-Raspberry Pi system presents a versatile, low-cost, field-deployable system that can be adapted for many audio and visual playback purposes. The low cost and the small learning curve make this set-up a feasible system for use by field biologists.

scholarly journals Low-Cost Image Search System on Off-Line Situation

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 153 ◽  
Author(s):  
Mery Diana ◽  
Juntaro Chikama ◽  
Motoki Amagasaki ◽  
Masahiro Iida
Keyword(s):  
Low Cost ◽  
Model Performance ◽  
Cosine Similarity ◽  
Raspberry Pi ◽  
Image Search ◽  
Search Tasks ◽  
Cost System ◽  
Network Properties ◽  
General Average ◽  
Fully Connected

Implementation of deep learning in low-cost hardware, such as an edge device, is challenging. Reducing the complexity of the network is one of the solutions to reduce resource usage in the system, which is needed by low-cost system implementation. In this study, we use the general average pooling layer to replace the fully connected layers on the convolutional neural network (CNN) model, used in the previous study, to reduce the number of network properties without decreasing the model performance in developing image classification for image search tasks. We apply the cosine similarity to measure the characteristic similarity between the feature vector of image input and extracting feature vectors from testing images in the database. The result of the cosine similarity calculation will show the image as the result of the searching image task. In the implementation, we use Raspberry Pi 3 as a low-cost hardware and CIFAR-10 dataset for training and testing images. Base on the development and implementation, the accuracy of the model is 68%, and the system generates the result of the image search base on the characteristic similarity of the images.

scholarly journals MTouch: An Automatic Fault Detection System for Desktop FFF 3D Printers Using a Contact Sensor

2021 ◽  
Author(s):  
Samuel Aidala ◽  
Zachary Eichenberger ◽  
Nickolas Chan ◽  
Kyle Wilkinson ◽  
Chinedum Okwudire
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Raspberry Pi ◽  
Ambient Conditions ◽  
Fused Filament Fabrication ◽  
Hall Effect Sensor ◽  
Cost System ◽  
Contact Probe ◽  
3D Printers ◽  
Contact Sensor

Desktop fused filament fabrication (FFF) 3D printers have been growing in popularity among hobbyist and professional users as a prototyping and low-volume manufacturing tool. One issue these printers face is the inability to determine when a defect has occurred rendering the print unusable. Several techniques have been proposed to detect such defects but many of these approaches are tailored to one specific fault (e.g., filament runout/jam), use expensive hardware such as laser distance sensors, and/or use machine vision algorithms which are sensitive to ambient conditions, and hence can be unreliable. This paper proposes a versatile, reliable, and low-cost system, named MTouch, to detect millimeter-scale defects that tend to make prints unusable. At the core of MTouch is an actuated contact probe designed using a low-power solenoid, magnet, and hall effect sensor. This sensor is used to check for the presence, or absence, of the printed object at specific locations. The MTouch probe demonstrated 100% reliability, which was significantly higher than the 74% reliability achieved using a commercially available contact probe (the BLTouch). Additionally, an algorithm was developed to automatically detect common print failures such as layer shifting, bed separation, and filament runout using the MTouch probe. The algorithm was implemented on a Raspberry Pi mini-computer via an Octoprint plug-in. In head-to-head testing against a commercially available print defect detection system (The Spaghetti Detective), the MTouch was able to detect faults 44% faster on average while only increasing the print time by 8.49%. In addition, MTouch was able to detect faults The Spaghetti Detective was unable to identify such as layer shifting and filament runout/jam.

scholarly journals Low cost audiovisual playback and recording triggered by radio frequency identification using Raspberry Pi

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e877 ◽  
Author(s):  
Ádám Z. Lendvai ◽  
Çağlar Akçay ◽  
Talia Weiss ◽  
Mark F. Haussmann ◽  
Ignacio T. Moore ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Raspberry Pi ◽  
Frequency Identification
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