Project

# Title Team Members TA Documents Sponsor
32 A Wearable Device That Can Detect Mood
 Junjie Ren
Kejun Wu
Peidong Yang
Xinzhuo Li
 design_document1.pdf
design_document2.pdf
final_paper1.pdf
final_paper2.pdf
proposal2.pdf
proposal1.pdf
 Said Mikki
# A Wearable Device That Can Detect Mood

**Team Members:**
- Junjie Ren [junjier2]
- Peidong Yang [peidong5]
- Xinzhuo Li [xinzhuo4]
- Kejun Wu [kejunwu2]

## Problem
Our project targets the pervasive impact of workplace stress, anxiety, and depression, recognizing these as critical challenges compromising individual well-being and overall productivity. Motivated by the need for proactive solutions, we aim to provide a wearable device equipped with advanced sensors and a unique mood recognition framework. By integrating psychological knowledge and wearable technology, our solution objectively monitors and manages mood-related challenges, offering timely feedback. The goal is to contribute to a healthier work environment, and our project represents a significant step at the intersection of technology and mental health in modern workplaces.

## Solution Overview

### Objective
The project aims to recognize and monitor the mood of employees in a workplace environment, leveraging wearable sensors and smartphone technology.

### Problem-Solving Approach
1. **Mood Recognition**: Using wearable sensors to collect physiological data that correlates with various mood states.
2. **Data Analysis**: Applying machine learning algorithms to interpret the physiological data and predict mood states.
3. **Feedback Mechanism**: Providing individual feedback to users and aggregated data to employers for well-being initiatives.

## Solution Components
1. **Wearable Sensor Subsystem**:
- **Components**: Practical sensors, like Toshiba Silmee™ Bar Type or W20/W21 wristbands.
- **Function**: Collects physiological data such as heart rate, skin temperature, and activity levels.
- **Role in Solution**: Provides the raw data necessary for mood prediction.

2. **Data Processing and Analysis Subsystem**:
- **Components**: Machine learning models (both personalized and generalized), feature extraction techniques.
- **Function**: Analyzes sensor data, extracts meaningful features, and applies machine learning techniques to predict mood.
- **Role in Solution**: Core of the mood recognition framework, turning data into actionable insights.

3. **Feedback and Reporting Subsystem**:
- **Components**: User interface for feedback, anonymized data aggregation for employers.
- **Function**: Provides mood predictions and wellness statistics to users and employers.
- **Role in Solution**: Closes the loop by informing users about their mood trends and assisting employers in enhancing workplace wellbeing.

## Criterion for Success

### Hardware Achievements
1. Successful deployment of advanced sensors, which are capable of collecting various physiological data such as heart rate, pulse rate, and skin temperature.
2. Integration of sensors into a wearable format that can be comfortably used in the working environment.
3. Clear and vivid display on the screen, indicating the detected mood.

### Software Achievements
1. Creation of a sophisticated mood recognition framework capable of identifying eight different types of moods at five intensity levels, with regular time updates.
2. Application of machine learning techniques for both personalized and generalized mood prediction models based on physiological data.
3. Achievement of a high average classification accuracy in mood prediction, showcasing the efficacy of the software algorithms.

## Distribution of Work
- Junjie Ren is responsible for System Design and Architecture.
- Peidong Yang is responsible for Data Collection and Analysis.
- Xinzhuo Li takes charge of Psychological Model Integration.
- Kejun Wu is responsible for User Study Coordination.

### Electrical Complexity
The project's electrical complexity encompasses integrating advanced sensors into the wearable device, demanding intricate signal processing algorithms, and robust coding for accurate mood interpretation. Ensuring seamless communication with the smartphone app adds complexity, along with implementing an efficient power management system for sustained monitoring.

### Mechanical Complexity
The mechanical intricacy involves designing a comfortable and durable wearable, accommodating integrated sensors while considering user ergonomics. The challenge lies in achieving a balance between functionality and aesthetics, ensuring the device is robust enough for daily wear and capable of withstanding various environmental factors. This complexity is justified by the need for a reliable, user-friendly solution contributing to mental health monitoring in professional settings.

simplified device for fasteners counter

Zhiwei Shen, Shuyang Wang, Yijian Yang, Jinsong Yuan

Featured Project

# PROBLEM DESCRIPTION

Lots of Industrial manufacturers need to realize real-time, efficient and accurate automatic counting of the assembly line products in the stages of production and transportation. On a standardized assembly line with stable operations, equal intervals and boxed objects the control system with infrared detection and microchip as the control core is effective and simple to implement. However, due to cost considerations, downstream manufacturers often prefer faster and less standardized assembly line operations during product inspection. Those unpackaged objects may have complex and changeable structures, and different kinds may have very similar structures. Moreover, the intervals and directions of these products on the assembly line are all random, which greatly increases the difficulty of monitoring, as well as achieving subsequent controlling purposes such as mechanical classification or equal-quantity loading.

After we discussed with people from a manufacturer, we realized their needs in this regard, so we decided to design an effective and low-cost device that realizes real-time monitoring and controlling towards specific industrial products with complex and random structures. From our investigations, we found that some factories use image recognition technology to achieve this goal, which turned out to be insufficient and costly because of their improper design. The manager of company complained about the stability, flexibility and fee of the traditional ways. After listening to the manager, we decide to implement our own ways to count line products, and our target is to increase the stability, flexibility and lower the cost.

By doing some research online, we confirmed that the most common monitoring system is still the infrared detection and microcontroller/PLC, which is effective for most assembly lines with products in boxes. And some newly developed approaches are based on cameras and computer vision, which we think are very potential but costly. Also, we found some other engineers still used simple infrared detection to achieve non-boxed objects monitoring. However, they met similar accuracy issues, like when two objects are too close to each other. Not to mention the objects that we are going to detect have much more complicated structures. In a word, we didn’t find any other monitoring system without using computer vision that can achieve our accuracy goal. So, our first major task is to come up with a better algorithm. We may also try pressure sensors, which is rarely used in assembly line object counting. In fact, we are going to investigate the feasibility of our idea by doing some experiments at their factory this week.

The scope of this specific problem might involve designing an embedded system with sensors and microcontroller unit to achieve the industrial control purpose, as well as programming and data analysis. Moreover, it may involve some knowledge about IoT because we also hope to use network module to transfer data and improve the automation level.

# solution overview

We plan to use infared sensor to dector the fasteners on the pipeline. We have two different kind of infared sensor in schedule. The first type could detect whether there exists objects within one meter, and the other one, which uses laser at the same time, can measure the distance between the surface of fasteners and the detector. The first one is cheaper but the second one could provide more imformation. We would choose in terms of real condition. There are also some alternative plans: we plan to use pressure sensor to count the total mass coming in and then calculate the number; acoustic rangefinder is another way to detect the distant in place of the second kind of infared sensor, and we will choose this plan if the original plan doesn't work so well.

Then, we plan to use PRI or PLC to process imformation. RPI is more powerful and enable us to write more complex code and develop some complicated functions such as classification of fasteners and nerual network which can analyze cutting pieces of fasteners, but PLC would be more stable in industry environemnt. The choice is mainly determined by real industry environment and the comments from manufacturers. We tend to use PLC to handle imformation from detectors and command the pipeline.

As for pipeline, workers put fasteners on the track. During the transportation, our device would count the number and in the end of pipeline, fasteners would be packed. After collecting enough fasteners, our machine would stop the pipeline.

# Solution Components

- Mono-chip（Raspberry Pi）

Price: around 300￥

Function: Receiving the data collected by the detector, processing it to get the number of fasteners that have passed, and transmitting the data to the remote-control center through the wireless interface.

We are going to use the neural network for modeling and use this model to count.

- Pressure-sensitive sensor

Price: 10￥-200￥

Function: Measuring the real-time weight on the sensor to assist in determining the number of products passed.

- Infrared sensor

Price: Already have

Function: Determining whether there is product passing.

- Laser rangefinder

Price: 60￥-200￥

Function: Measuring the distance between the product to the boundary of the conveyor belt.

- Acoustic rangefinder

Price: 200￥-300￥

Function: Measuring the distance between the product to the boundary of the conveyor belt.

- Remote-control Center

Price: Already have

Function: Receiving the data transmitted by the mono-chip, presenting the past products so far, and commanding every component according to that.

# CRITERION FOR SUCCESS

- High accuracy is required. The counter should have a error rate at 1%+-0.1%.

- The classifier is supposed to perform well, then the device can be migrated to a similar pipeline. The device is a kind of baler. When the input products are not of the same kind, if there is no classification function, packaging errors are likely to occur.

- The process of counting and classifying should take less time.

- The devicey should be stable enougth to be used in manifacture.

- Additional Function: Operator can control the machine and see results easily and remotely.

# sponsor

This project is well connected to industry. The company that sponsors us is 杭州六联机械科技有限公司(Hangzhou Liulian Machinery Technology Co., Ltd.) and the manager with whom we talked is 杨向峰(Xiangfeng Yang).