PRE2025 1 Group1

Name	Student number	Study	E-mail
Robert Arnhold	1847848	Mechanical Engineering	r.w.arnhold@student.tue.nl
Sietse Bosman	1894013	Applied Physics	s.bosman@student.tue.nl
Octavian Astefanei	1836374	Electrical Engineering	o.astefanei@student.tue.nl
Anne Willems	1631810	Electrical Engineering	a.m.j.e.willems@student.tue.nl
Kerim Gjergjizi	1813420	Electrical Engineering	k.gjergjizi@student.tue.nl
Lucas Spronk	1563564	Computer Science	l.spronk@student.tue.nl

Plan

Concept Introduction

This project aims to design and prototype a smart hamster cage. Hamster cages have long featured similar designs, features and limitations. While a hamster wheel may give the hamster the ability to exercise, a water bottle give it the ability to drink and a food bowl the ability to eat, these are all parts of the hamster cage that are long outdated. In our modern world there is a market gap: a hamster cage that can help care for your hamster, teach you more about it, and let you better interact with your beloved pet. If successful, this proof-of-concept opens the door to an expansion towards larger pet enclosures such as rabbits, guinea pigs, and more.

Objectives

• Provide reliable care for the hamster, even while users are away
• Help users more effectively care for their hamsters
• Other, user-defined objectives that will be found via user research

Users

General Target Audience:

The target audience consist of hamster owners themselves, as well as anyone else involved in caring for the hamster, e.g. owner's family or roommates.

People of various ages own hamsters, but they are mostly "millennials, aged 25-39", who "make up about one in three small-pet owners" (also includes fish, birds and some other animals) [1]. There is a strong correlation with younger aged owners, especially the presence of household members under the age of 12; "approximately 72% of hamster owns are under the age of 44" [2], and "nearly 90% of households with hamsters include children, and 87% of those households have children under 12 years old" [3].

While small-pet owners in the U.S. tend to earn lower incomes than other pet owners [2], they actually spend more on their animals. On average, small-pet households spend about 252 USD/month, compared to 140 USD for dog owners. Spending patterns also vary by demographics: men spend slightly more than women, married owners spend slightly more than singles, and younger owners (18–24) spend the most overall (174USD/month), with pet spending declining gradually with age [4]. The exception to this spending trend is the age group of 14-17 with the lowest average spending of 73USD/month [4]. While there is a general willingness to spend upwards of 100USD on small pets every month, this also highlights the need to keep costs manageable for younger owners with limited budgets.

Finally, owners of small pets are most commonly "urban dwellers, particularly those [living] in apartments or smaller living spaces". They "disproportionately own compact pets due to space constraints" [5], and since hamsters are quite restricted in their spacial requirements compared to other larger animals or animals with special living conditions, hamsters are very popular in this demographic category. The majority of families with children live in suburban single-family homes with more space, however the smart hamster cage should not significantly exceed the dimensions of a regular cage to ensure product suitability for those living in more compact environments such as apartments and student housing.

To conclude, a preliminary target market can be narrowed down to users between 12 and 44 years old, typically either children or their parents, who either care for the hamster themselves or live with family who helps take care of the pet. Users will typically be willing to spend upwards of 73 to 137USD/month, which suggests a further increased price range for a one-time purchase.

Example User Profiles:

Emma, 12 years old (student)

• Recently got her first hamster as a pet.
• Parents want her to learn responsibility but also want to ensure pet safety.
• Would benefit from automated feeding/water alerts and activity data.
• Likes fun graphics showing hamster’s daily life.

Michael, 28 years old (busy professional)

• Works long hours and sometimes travels for a few days.
• Wants peace of mind that his hamster has food/water when away.
• Needs reliable notifications and refill reminders.
• Less interested in visuals, more in core “care assurance” features.

Sofia, 38 years old (parent of two children)

• Sarah has two children, ages 7 and 10.
• Children are responsible for daily care but Sarah supervises to ensure the hamster’s well-being.
• Values tools that make pet care educational and fun.
• Wants quick access to reliable information to monitor the hamster throughout the day.

State-of-the-Art

A low-cost automated apparatus for investigating the effects of social defeat in Syrian hamsters [7]

Relevant points/summary:

The article is about an automated apparatus that can used to investigate the effects of defeat in hamsters. They use low power lasers and laser detectors to keep track of the position of the master. A computer measures three things using the lasers\detectors: average position, frequency of visits to each chamberand the duration of these visits, and the frequency of changes in direction of travel in each chamber. The data collection program they use is MEDPC for windows.

An Open-Source, Automated Home-Cage Sipper Device for Monitoring Liquid Ingestive Behavior in Rodents [8] 

Relevant points/summary:

Common approaches for measuring liquid intake:

-       Computer-tethered lickometers

-       Video based systems

-       Measuring or weighing the liquid

In the article they made a photobeam-based sipper device.

Advantages:

- battery powered (battery life of > 2 weeks)

- fits in vivarium caging

- quantifies the intake of two different liquids simultaneously

-  low cost and easily constructed

- provides data with high temporal resolution (allows for detailed analysis of drinking patterns)

- open source

Disadvantage

-       Animal interactions that are picked up by the sensor are not limited to licks

In the article they use the device to measure the volume of liquid ingested, whether there is a preference for chocolate milk vs water and confirm that the sipper device can be succesfuly integrated with in vivo measurements.

A System for Monitoring Animals Based on Behavioral Information and Internal State Information [6]

     Relevant points/summary:

     In this paper, a monitoring system for animals using video image analysis is used. It  extracts features related to behavorial information and the animal internal state via mask R-CNN. These features are used to detect typical daily activities and anomalous activities. This way it can detected when the hamster behaves in an unusual way. The system also combines a new feature extraction method using deep neural network techology and an anomaly detection method.

The systems consists of 4 parts:

-       Signal measurement: a digital hi-vision video camera

-       Feature extraction: mask R-CNN and color information detection

-       State discrimination: one class SVM

-       Display: PC with an HDMI capture device

To test the system a loud sound was generated and the hamsters reaction was measured.

The discrimination rate of non-daily activities did not have a high level of accuracy. Mainly because the hamster shrank during the daily activities and the direction vector was often inverted because of the noise caused by the hamster’s shadow. The training data also includes infrequent situations in daily activities, which makes it possible that the discriminative boundary was not accurately determined

Design and Development of a Smart Pet Feeder with IoT and Deep Learning [9]

Relevant points/summary:

Automatic pet feeder was developed using internet of things technolgy and deep learning to address feeding challenges. The system makes sure the pet receives the right amount of food, regardless of whether the owner is available. This makes sure the animal stays healthy, because an owners busy schedule can often lead to inconsistent feeding, which can cause malnutrition or obesity. It is also offers pet owners convenience and peace of mind by enabling the feeding process in their absence. 

The system architecture:

-       Weight sensor (Tecneu HX711): determines pets weight and food weight

-       Camera: detects which species

-       Ultrasonic sensor (HC-SR04): measures distance to the pet from the feeder.

-       Servo motor: controls dispensing mechanism

-       Arduino (mega 2560): microcontroller that handles all the actuators and sensors.

Areas of improvement that were mentioned:

The system could learn from a pets’ eating patterns and adjust feeding schedules and portion sizes over time and adding other pet identification, since only dogs were used to train this system.

A Novel Automated System Yields Reproducible Temporal Feeding Patterns in Laboratory Rodents [10]

Relevant points/summary:

In this article the aims was to develop and validate a reliable method for supplying crushed diets to laboratory rodents in consistent, relevant feeding patterns for prolonged periods.

They used 2 different feeding patterns:

-nocturnal meal-feeding

-nocturnal grazing

The cumulative food intake for both methods was the same.

PiE: an open-source pipeline for home cage behavioral analysis [11]

Relevant points/summary:

In this article PiE is introduced, an open-source, end-to-end, user configurable, scalable, and inexpensive behavior assay system. It has a custom-built behavior box to hold a home cage, as well as software enabling continuous video recording and individual behavior box environmental control. The PiE system reduces environmental and experimenter disruptions by providing fully remote control and monitoring of home cage behaviors. The system was tested on mice. The sensors and actuators that were used were  a ceiling mounted, downward facing, infrared (IR) video camera (Pi NoIR) to allow both daytime and nighttime video recording, (2) white and IR LEDs to illuminate the behavior box (Note: to match the Pi NoIR camera sensor, 940nm or shorter wavelength IR LEDs are preferred for optimal nighttime illumination), (3) a temperature and humidity sensor, and (4) a circulating fan for climate stability, which were controlled by a raspberry pi. Desktop software, called VideoAnnotate, was used to perform behavioral analysis.

CageView: A Smart Food Control and Monitoring System for Phenotypical Research In Vivo [12]

Relevant points/summary:

The article introduces an automated and smart system (named CageView) used to monitor a mouse, detect motion, and control access to food in accordance with experimental schedules. Cageview accomplishes it food control and activity monitoring via:

1. An actuator for linear displacements of the food access door controlled by a custom-designed interface to set the feeding and fasting schedule;

2. A vision unit with visible and near-infrared cameras and a near mid-infrared LED for day and nighttime monitoring, which forms a video streaming and data transmission system using wireless or wired communication networks; It also uses a a Raspberry Pi single-board computer.

3. A trained convolutional neural network (CNN) that detects the animal position in the image which enables movement measurement.

The feeding mechanism is designed to be mounted on conventional cages used in most research centers and has a sliding mechanism that can enable or disable the animal’s access to food. Activity measurement is done via making an activity heatmap and distance measurement.

The CageView technology has been disclosed in the Maddahi Y. and Maddahi A. Methods and apparatus for monitoring, feeding, and checking animals. United States Patent. US 63/321,368, 2022

Feeding Experimentation Device (FED): Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents [13]

Relevant points/summary:

In the article they provide a description of a solution for measuring food intake by mice,  FED. FED is an open-source system that was designed to facilitate flexibility in food intake studies. It is compact and battery powered so it can fit in standard home cages.  The mice are fed using food pellets. When a mouse removes a pellet, a photointerrupter sensor sends a signal to the microcontroller and the time-stamp is logged on the onboard secure digital (SD) card. A new pellet is dispensed via the motor. The feeding can be limited to specific times of the day. The limitations of the FED:

-       SD card can be a cumbersome means to track and store data from many FEDs

-       Pellet jams could occur

-       Users should never leave mice with FED as their only food source without checking FED's functionality daily

A single FED can be assembled for approximately $350.

An open source automatic feeder for animal experiments [14]

Relevant points/summary:

In the article they describe an open source experimental feeder using an Arduino microcontroller. This feeder can be used for most sizes of dry food pellets with the potential modification of a single component;  They also describe the buidling procedure of the feeder. They tested it on pigeons, monkeys and cats. The cost for building this feeder is less than 200 euros.

The Promise of Automated Home-Cage Monitoring in Improving Translational Utility of Psychiatric Research in Rodents [15]

Relevant points/summary:

In the article they describe three of the most commonly used approaches for automated home cage monitoring in rodents and review several commercially available systems that integrate the different approaches. Automated home-cage monitoring records rodent behavior in their home cage over extended periods using minimal human contact.

Approaches:

-       Operant wall systems: detect nose pokes using an infrared beam and deliver food pellets or liquid in response to learned behaviors. Behaviors are directed by light or tone cues controlled by the operant wall.

-       computerized visual systems: CVS monitor behavior at high temporal resolution over extended periods including the dark and light phases of the circadian cycle, and trainable software are publicly available. The trainable nature of CVS allows for extensive flexibility in terms of the behavioral phenotypes it monitors. Limitations of the CVS include the requirement for adequate contrast between the rodent and its background and the need for most systems to house animals individually

-       Automatic motion sensors: use motion sensors to assess locomotor activity.  AMS generate data about distance traveled, velocity, and time spent at specific locations within the cage. They are unobtrusive and are not affected by lighting conditions, allowing for reliable data collection during the dark cycle on locomotor activity and sleep behavior

LocoBox: Modular Hardware and Open-Source Software for Circadian Entrainment and Behavioral Monitoring in Home Cages [16]

This article introduces LocoBox, an affordable, open-source system combining hardware and software to control light–dark cycles and monitor locomotor activity in home cages—ideal for circadian rhythm research MDPI+1. LocoBox empowers circadian researchers with a flexible, scalable, and budget-friendly tool. It allows for custom entrainment experiments and long-term behavioral monitoring—all using open-source solutions that can be easily adopted or adapted.

Key Features

• Hardware: Each LocoBox is a light-sealed Plexiglas enclosure equipped with:
  • An LED (with heat sink and diffuser) for programmable lighting
  • A passive infrared (PIR) sensor for detecting animal movement
  • A silent ventilation fan and real-time clock (RTC)
  • Managed by an Arduino Mega 2560 and daisy-chained power design for easy scalability MDPI+1.
• Software:
  • Arduino-based firmware ensures accurate timing and control of light phases.
  • Python GUI (using Tkinter and pySerial) allows users to define up to 12 sequential lighting “phases”, including flexible T-cycles (non-24-hour cycles), simulate jet lag, seasonal variations, etc. — and replicate schedules across up to five boxes per controller MDPI+1.
• Data Logging & Visualization:
  • Activity and light state are logged every minute in TSV format.
  • Built-in tools enable generation of double-plot actograms and spectral heatmaps to visualize power spectral density and phase angles over time MDPI+1.
• Accessibility & Scalability:
  • Replicable with common, low-cost components (~USD 100–150 per box).
  • Entire system (hardware blueprints, Arduino and Python code) is freely available on GitHub MDPI+1.
  • Suitable for long-term and parallel experiments, democratizing circadian research even in resource-limited labs MDPI+1.

Automated Home Cage Monitoring of an Aging Colony of Mice—Implications for Welfare Monitoring and Experimentation [17]

Overview

This study leverages Home Cage Monitoring (HCM) using a Digitally Ventilated Cage (DVC) system to track activity patterns and rest disturbance indices (RDI) in an aging colony of male and female C57BL/6 mice, offering insights relevant to both welfare and experimental design Frontiers. This research demonstrates the value of high-resolution, long-term automated monitoring of mice. It reveals dynamic changes in activity and rest behavior with age, responses to cage handling, and capability to flag welfare-related behaviors—underscoring HCM’s promise for humane, data-rich research.

Generally not that interesting on the hardware side, as it is too different from what we want to use. Instead this article can be used for reference for psychological and behavioural data collection and processing.

Methods

• Mice were housed in DVC systems and monitored longitudinally up to 18 months of age.
• Used statistical models (linear mixed models) to assess habituation, aging, and effects of cage changes.
• Stereotypic behaviors were identified through visual inspection of activity spikes Frontiers.

Key Findings

• Upon arrival, mice showed high activity and RDI during the light phase and reduced activity during the dark phase, normalizing to typical circadian behavior after several days.
• Age-related changes: Activity decreased from 5 to 14 months, then rebounded toward baseline levels.
• Stereotypy detection: Cages flagged for stereotypic behavior exhibited sustained activity spikes, especially pronounced during the dark phase.
• Cage changes caused increased activity and RDI during the light phase, but did not significantly affect the dark phase—this pattern was consistent over time across ages Frontiers.

Implications

The findings:

• Illustrate how HCM can detect distinct behavioral changes across aging stages.
• Highlight the potential of automated monitoring systems like DVC to detect early welfare concerns (e.g. stereotypic behavior).
• Support the use of HCM for improving experimental rigor and animal welfare in long-term studies Frontiers.

Rodent Behavioral Assessment in the Home Cage Using the SmartCage System [18]

This system termed SmartCage measures rodents’ behavior in their home cages as a significant endpoint. The SmartCage system consists of multiple instrument platforms that interface with ordinary rodent home cages. Each SmartCage is comprised of multiple sensors including a floor-vibration sensor, an infrared (IR) matrix and flexible modular devices. This system is noninvasive and allows the animal to be tested in its home cage that has bedding, food, and water, making it appropriate to monitor animals for days or weeks. The automated measurements include wake and sleep/inactive states. The active parameters include locomotion, rearing, and animal movement patterns, for example, rotations (cycling).

A System for Monitoring Animals Based on Behavioral Information and Internal State Information [19]

In the current study, a system is proposed that discriminates the state of an animal by combining two types of features: behavioral information, which determines the kind of movement performed by the animal, and internal information, which reflects the animal’s internal state. The proposed system measures biological information from camera images and extracts features to discriminate states using machine learning. In addition, a neural network was used to increase the accuracy of detecting the target, and an anomaly detection method was used to perform discrimination. In the experiments performed, video images were prepared that contain routine behaviors and generated a model that can detect non-routine behaviors using the proposed system. We also prepared video images including non-routine behavior in which the hamster was stimulated by clapping to generate an abnormal sound. In the video, the behavior changed significantly before and after the stimulus presentation, and the extracted feature values also changed according to the behavior. As a result of detection, the system discriminated the behavior as non-routine behavior after the stimulation. In conclusion, the results supported the possibility of using a pet monitoring system to immediately inform the owner when the animal is under load.

The Promise of Automated Home-Cage Monitoring in Improving Translational Utility of Psychiatric Research in Rodents [20]

Type & Scope: Mini-review highlighting the potential of automated home-cage monitoring (AHCM) for psychiatric research.

Key Points:

• Problem addressed: Traditional behavioral tests for psychiatric phenotypes often rely on brief sessions (<10 min), are sensitive to environmental and experimenter-related factors, and yield poor reproducibility and translational reliability.
• Solution proposed: AHCM enables continuous, minimally intrusive monitoring within the home cage, generating large datasets that span circadian and estrous cycles, reduce human influence, and increase sensitivity to behavioral changes.
• Approaches reviewed:
  • Operant Wall Systems (OWS) – Sensors for nose pokes that deliver rewards; suitable for tasks such as timing and working memory.
  • Computerized Visual Systems (CVS) – Video-based tracking for locomotion and exploration.
  • Automatic Motion Sensors (AMS) – Infrared or motion detection to capture activity levels.
• Commercial platforms such as IntelliCage, PhenoTyper, Actual-HCA, and Chora Feeder integrate these methods for group- or individual-housed rodents.
• Advantages: Better reproducibility, high temporal resolution, social-context cued behavior tracking, and improved translational validity.
• Conclusion: AHCM offers a more robust alternative to brief traditional tests, with recommendations to integrate, validate, and expand use of these systems in pre-clinical psychiatric research.

Comparison of automated home-cage monitoring systems: emphasis on feeding behaviour, activity and spatial learning following pharmacological interventions [21]

Relevant points/summary:

This study assessed the effectiveness of three different observation systems as methods for determining strain and pharmacological induced differences in locomotor activity, feeding behaviour and spatial learning.

The test subjects in this study were mice.

Observation systems that were used:

-       PhenoTyper: utilises video-observation to record movement of individual subjects. In the test they measured the total distance moved in hourly bins following drug treatment and the time spent in the food zone

-       PhenoMaster:  relies on infrared sensors for the detection of horizontal and vertical activity. In the test they measured the food consumption and the total distance moved in hourly bins prior to and for the 12 h following drug treatment. It has weight transducers, for food and water measurement.

-       IntelliCage: does the monitoring and registration of activity of micro-chipped animals via antenna-containing tubes for entry into activity corners. In the test they measured the number of visits to rewarded corner in hourly bins prior to and following drug treatment and the total number of visits to all corners.

The benefit of using different home-cage observation systems is that in addition to assessing exploratory activity by way of different tracking parameters each system has distinctive recording features which could be used to complement one another and facilitate characterization of mice and pharmacological induced changes.

An Open-Source, Automated Home-Cage Sipper Device for Monitoring Liquid Ingestive Behavior in Rodents [22]

Type & Scope: Methods-focused research introducing an accessible tool for monitoring drinking behavior in rodents.

Device Overview:

• A photobeam-based, battery-powered, wireless “sipper device”, implantable directly in the home cage.
• Supports tracking of intake from two liquids simultaneously (two-bottle preference assays).
• Designed to be low-cost (< $100), easy to build, open-source, and readily customizable PMCPubMed.

Validation Experiments:

1. Calibration: Device performance correlated strongly with time-lapse video measurements of liquid consumption.
2. Preference testing: Successfully measured rodents' preference for water vs. chocolate milk.
3. Neural integration: Compatible with fiber photometry, enabling simultaneous neural activity tracking during ingestion behavior PMCPubMed.

Advantages:

• Highly scalable due to low cost and simplicity.
• Suitable for long-term and circadian monitoring (battery life ~2 weeks).
• Open-source nature enables wide adoption and adaptation

Development of Eight Wireless Automated Cages System with Two Lickometers Each for Rodents [23]

Relevant points/summary: In this article they present a low-cost alternative for a lickometer system that allows wireless data acquisition of licks from eight cages with two sippers each.

They present three development and validation steps:

-       selection of the proper licking detection sensor:

All prototypes consisted of an Arduino MEGA, used for the analog/digital conversion (A/D converter) of the sensor’s signal, a Secure Digital (SD) card module to store the data, a realtime clock module (DS3231) to precisely time the licks events and a Liquid Crystal Display (LCD) 16 x 2 to show the number of licks and date/time of occurrence.

Prototypes:

1. light dependent resistor (LDR): was discarded, did not have adequate stability because of environmental luminosity.

2. photo electric: precision was 77.02 6 11.69%

3. capacitive (touch): precision was 91.14 6 5%

The prototypes did not statistically differ, the capacitive prototype was chosen.

-        translation to wireless transmission and validation with emulated signals

 The in silico validation step with simulated licks at extreme and typical rates was proposed. It is important to verify whether other wireless equipment is turned off to avoid loss of information.

-        in vivo validation using mice drinking in two-bottle lickometer cages.

Limitations to system:

- The device does not measure volume

- manufacturing the lickometer and installing the software requires knowledge beyond behavioral neuroscience

Approach

Potential Features

• A camera needs to be added to the hamster cage, which such that the owner can monitor the hamster off site.
• A automatic feeder needs to be added, which adheres to a healthy feeding pattern. In a way that the availability of the user is not required for the feeding.
• A measurement system for the food and water intake needs to be added. This to spot abnormalities in the eating pattern of the hamster.

Planning

Week 1	Week 2	Week 3	Week 4	Week 5	Week 6	Week 7	Week 8
Course introduction and formation of the team	Research on the selected topic	Interview possible users	Interview possible users	Work on and test the prototype	Work on and test the prototype	Finish the prototype and do final testing	Finish wiki
Brainstorming and deciding the project idea	Create interview questions for possible interested people	Update the wiki page with the data gathered from the interviews	Decide additional characteristics based on the interviews	Research	Research	Work on the presentation and demo	Final presentation and demo
Research on the selected topic	Decide some of the main characteristics of the product	Decide a possible price target for the project and realistic objectives	Start designing the prototype	Update the wiki page	Update the wiki page	Update the wiki page
Research for alternatives and state-of-the-art	Update the wiki page with interview questions	Decide additional characteristics based on the interviews	Decide on possible architectures and components
Update wiki page with planning, users and literature study

Milestones

The team's main milestones are:

• Finish conducting the interviews in order to model out prototype based on the opinion of the possible customers;
• Deciding the list of characteristics that our product have have in order to satisfy as many customers as possible;
• Finishing and testing the working prototype;
• Finishing the presentation, demo and wiki page.

Deliverables

The team's main deliverables are:

• Wiki page
• Working prototype
• Final presentation

Task Division

Name	Study	Task Focus
Robert Arnhold	Mechanical Engineering	Mechanical design and prototyping
Sietse Bosman	Applied Physics	Simulation & modeling of parts and prototype
Octavian Astefanei	Electrical Engineering	Electrical design and assembly of electrical parts/features
Anne Willems	Electrical Engineering	Electrical design and assembly of electrical parts/features
Kerim Gjergjizi	Electrical Engineering	Electrical design and assembly of electrical parts/features
Lucas Spronk	Computer Science	Programming of digital part systems, companion software

Bibliography

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Weekly Tasks

Week 1

Name	Total Time Spent [hours]	Task Breakdown
Robert Arnhold	6	Two weekly meetings (1h each), Plan sections (4h, Concept Introduction, Objectives, Users)
Sietse Bosman		Two weekly meetings (1h each),
Octavian Astefanei	6	Two weekly meetings (1h each), Planning, Milestones, Deliverables (2h), Research on existing technology (2h)
Anne Willems	14	Two weekly meetings (1h each), part of SoTa (12h, read and summarized articles: [6],[7],[8],[9],[10],[11],[12],[13],[14],[15],[23],[24])
Kerim Gjergjizi		One weekly meetings (1h),
Lucas Spronk	5	Two weekly meetings (1h each), part of SotA

Week 2

Name	Total Time Spent	Task Breakdown
Robert Arnhold
Sietse Bosman
Octavian Astefanei
Anne Willems
Kerim Gjergjizi
Lucas Spronk