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https://github.com/TUDelft-SPC-Lab/spcl_midge_hardware

Hardware design files for the SPC lab MIDGE wearable devices for in-the-wild social data collection.
https://github.com/TUDelft-SPC-Lab/spcl_midge_hardware

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Hardware design files for the SPC lab MIDGE wearable devices for in-the-wild social data collection.

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README 

        
# SPCL Midge badge



Welcome to the Hardware repository of the Social Perceptive Lab for the MIDGE badge.



![Test](./Media/overview.jpg)



The Midge badge is a 55x35mm wearable PCB for in-the-wild social data collection. 

It features

- 2 microphones, with adjustable low and high cutoff frequencies

- 9 axis IMU

- SD card storage

- 300mAh battery

- BMD 300 processor with bluetooth low energy



Learn more about the project itself [here](https://conflab.ewi.tudelft.nl/)



Learn more about the software [here](https://github.com/TUDelft-SPC-Lab/midge-code)



## Motivation



#### Why are conference-badge-type wearable sensors important for us?

Wearable badges are a cheap, convenient way to record high-fidelity signals from individuals in social interaction. We have the need for such a device in the SPC lab because we study the social dynamics of small crowds (~25-100 people) interacting during an event. Conference badges allow us to equip every participant to record individual audio, body movement signals and proximity information, all at a relatively low cost per device.



#### Why not use cell phones?

Cell phones can be useful in sensing crowds but differences in the sensors, fidelities and recording options / software available to different cell phone models are an issue for analysis. If cell phones are to be provided by the scientist, then cost becomes an issue in comparison with wearable badges.



#### Why not use a wrist wearable?

Wrist wearables can be effective at capturing hand movements. While we could have designed our device with a wrist form factor, we think the badge form is more convenient to use and its position on the chest allows it to more reliably capture proximity and overall body movement through acceleration.



#### Why did we decide to modify the Rhythm Badge?

The open-source Rhythm Badge was designed with office environments in mind, where the goal was the collection of longitudinal data about a team over days or weeks. Proximity information and a low-frequency microphone allowed it to reconstruct the social network of team members. On the other hand our goal was to computationally analyse interactions both at the group and individual level from social signals (speech, movement, proximity) of subjects. This required higher fidelity sensors and a new device design.



#### How did we modify the  Rhythm Badge?

- Enabled full audio recording, with a frequency up to 48KHz with an on-board switch to allow physical selection between high and low frequency. 

- Added a 9-axis IMU to record pose. IMUs combine three tri-axial sensors: an accelerometer, a gyroscope, and a magnetometer. These measure acceleration, orientation, and angular rates respectively. The sensor information is combined on-chip by a Digital Motion Processor.

- Added an on-board SD card to directly store raw data, avoiding typical issues related to packet loss during wireless data transfer.



## What makes your project stand out?

This project is unique because it improves upon the state-of-the-art wearable sensors measuring face-to-face human social interactions. The improvement points are catered towards more fine-grained and flexible capturing of data. For instance, the choice between high and low frequency audio capturing mode is provided at data acquisition and the experimenters can freely choose the frequency best suitable for them (for example, whether they have high-frequency audio for transcription or privacy-preserving low-frequency audio for detecting speech activities). Additionally, the inclusion of the on-board digital motion processor directly provides orientation estimation with the newly included full 9-DOF inertial motion unit (IMU). This allows future applications (e.g., real-time F-formation detection) to be built directly on top of the outputs of the DMP. The above design considerations and implementation exemplify the uniqueness and new advantages that Midges offer. 

## Status of the project

### Issues on the PCB

Currently we are not aware of specific problems with the PCB, all functions work correctly (battery charging, cut-off switches, SD card, audio recording, IMU recording, status LEDs. If you do find a problem, please open an issue in the github repository.

### Tests or results from the project

The design of the Midge is complete and X devices have been created. The device was successfully used in one large data collection in a computer science conference to record audio (1200Hz), IMU data (56Hz) and proximity data (1Hz) for 48 data subjects in the same space. This allowed us to identify several important bugs that have been subsequently fixed. The dataset is in the process of being published. The devices have also been used by several student groups with the purpose of validating the sensor information. 

### People or parties involved in the development

The development of the device was contracted to Ioannis Protonotarios (https://protonotarios.info/) for the Socially Perceptive Computing Lab at Delft University of Technology. Ioannis took as base the latest release of the MIT Media Lab’s Rhythm badge (https://www.media.mit.edu/posts/rhythm-badge/) and implemented both hardware and software changes and updates. Testing of the device was done in conjunction with Socially Perceptive Computing Lab members. The whole process took X months and two iterations of the device’s hardware.

### Do you want people to contribute to the project?

Absolutely. Some of the contributions that we current find most important in our application setting (sensing of small crowds) are:

Improvements to audio sensing. Although our Midge was an improvement over the Rhythm badge in quality of audio sensing, audio quality is still an issue. Especially, the front-facing microphones of the device introduce significant cross-contamination. Potential solutions include exploring beam-forming, directional microphones or addition of headset support.

Software improvements. There is significant room for improving the software of the device. The hub, which serves as monitoring and control centre for all connected Midges is especially relevant. The current command-line interface would benefit from being replaced by a user-friendly web-based interface that displays the status of connected midges and allows for quick bulk actions like eg. disabling sensors, starting/stopping recordings, etc.