Introduction: Aman - Stress Monitoring Sock

About: cofounder of RenaiSense Ltd

Stress is something that all but the best of us undergo everyday and the increasing work hours is not exactly helping. Aman is a low cost connected e-textile based sock that comfortably and non obtrusively quantify stress and aid people with hypertension identify and manage their stress. Aman does so by measuring Electrodermal Activity(EDA) and Heart Rate. The problem with the existing Fitness trackers is the lack of context which sampling, Without context interpretation of the data becomes difficult.

Aman measures context using environmental parameters like Temp,Hum& Motion. EDA is a part of the commonly known "lie detector" and even misused in circles of scientology(E-meter), EDA however is an important parameter that would help us quantify stress. Various products elucidate the use of EDA such as the Empatica Embrace which is used for Epilipsy monitoring.

Step 1: The Problem

Stress has become a growing concern in modern society as the job environment and education are becoming more competitive. Long term consequences of the same are often ignored leading to a plethora of psychological and physiological problems if left unchecked. Stress monitoring and periodic feedback can help people better manage their stressors by providing easy to use tools. Sweating is a biological mechanism which is controlled by physiological and psychological activity. Emotional stress can be correlated with sweating activity.

Step 2: Existing Systems:

Existing devices have been bulky and have primarily been used for clinical diagnosis. To perform continuous monitoring however user’s comfort plays a major role during development of such a device.

The current commercially available EDA sensing tools are bulky and comprise of a separate amplier and Analog to digital convertor along with a data acquisition device. The external electrodes used are uncomfortable for long use. The current systems include edaMove, Empatica Embrace, CPSpro EDA tool. The Embrace and edaMove have explored wearable wristworn designs and though an improvement to previous designs, they measure only EDA and are quite expensive for consumers. One of the major reasons for discomfort is the use of metal plate electrodes or gel type AgCl electrodes while measuring EDA.

Step 3: Solution:

My goal is to quantify stress in a person by measuring Electrodermal Activity, Heart Rate and Heart Rate Variability continuously in a person invisibly in a Sock. Along with this motion data along with ambient Temperature and Humidity is measured. Use of continuous monitoring aids in preventative healthcare diagnostics and use of data sharing with loved ones can aid in forging better relationships and handle stress better. This form of quantified stress tracking can be correlated specific events which when digitised allows not only raw records of the event but also how the person reacted to the same. Contextual information such as this can be viewed by the user later to reflect on the events and alter behavior using machine learning.

Dry fabric electrodes have been shown to improve comfort from the standpoint of the user while compared to standard metal electrodes and thus we aim to use it.

Photoplethysmography would be done on the big toe validated here, Textile connectors would snap into the toe sensor enabling quick removal before washing.HRV and Heart Rate would be calculated by measuring the reflectance of the 535nm light in the toe. Piezoresistive strips would be used to provide information on gait and motion data.

Environmental information such as temperature, humidity would be sensed by a HTU21 sensor. Motion data and gait would measured using the Eeonyx Piezoresistive textile which would be lined along joints and the toe to enable better filtering of PPG against blood dynamics due to motion.

Step 4: System Design:

The Electrodermal activity signal consists of two parts the tonic and phasic activity, the tonic portion of the EDA signal is slow varying and is called the Skin Conductance Level (SCL) which corresponds to physiological the phasic activity is quick reacting and are associated with short term events and in response to cognitive and sensory input the sudden peaks are known as Skin Conductance Responses(SCRs). Besides this the physiological activity is a contributing factor while monitoring EDA.

We have used conductive textile based sensors stiched inside the sock with two electrodes placed an inch away in the arch of the foot. This has been validated by research in Wearable's lab ETH.

For initial evaluation and testing, We just use a resistor divider along with a low pass filter ceramic capacitor before interfacing to the ADC.

Step 5: Components:

  1. Pulse Sensor Arduino based open source photo-plethysmogram sensor
  2. Conducitve jersey fabric Electrodes for Electrodermal Activity
  3. Particle Photon STM32 + WiFI chipset 'nuff said
  4. HTU21 module Accurate I2C temperature and humidity sensor
  5. Toe socks Japanese Tabi socks for the removable pulse sensor design
  6. 500mah 3.7V LiPo Battery
  7. 5V polulu boost convertor for powering photon

Step 6: Heart Rate Sensor:

A pulse sensor amped from World Famous Electronics was chosen for it's simple yet effective performance. Also the sensor coincidentally has three cables coming out of it making it ideal for use with the three channel textile cable. We measure it in the big toe using a small removable toe socks part which can be removed along with the electronics parts before washing. I'm attempting to move to a transmission type heart rate sensor in the future.

I have soldered one end of the cable to a three pin snap which would hook up with the heart rate sensor while also being removable.Temperature and Humidity sensing:We measure temperature and humidity to get context about the environmental parameters around the person, This is important to find as it helps to separate psychological from physiological stress. To this end we aim to incorporate a HTU21 I2C Temperature and humidity sensor in a clip like design in the shoe facing the environment.

For sensing gait we use an ADXL345 along with piezoresistive bend sensors which would be integrated to the socks. This would monitor walking posture and amount of activity exerted which would be helpful to separate cases where user sweats due to a run than sweat due to stress.

Step 7: Hardware & Communication:

For prelimany data gathering and sensor interfacing I've been working with a particle photon which sports a STM32 along with an effective broadcom wifi chipset. This at the initial stages would be reporting to thingspeak server along with with tensorflow in the future.The future iterations would use a Nordic Semiconductors nRF52832 BLE Micro controller for its extremely low power consumption and low cost. Further due to the presence of NFC in the MCU, Pairing would be extremely simple for the user.

The data can either be transferred through the user’s smartphone to the cloud for analysis.

Step 8: Power Management & Battery:

For the prototype we have used a 500mah lipo to power the power demanding particle photon through the Pololu 5V boost convertor. Once I move to a nRF52 BLE MCU, A coincell would suffice making the wearable even smaller.

Step 9: Scenario

The comfort of the user was a priority while designing AMAN to enable long term continuous use of the wearable. We took inspiration from the Sensoria Smart sock in the preliminary design and fused our own ideas into this. The Heart rate sensors and the electronics are easily disconnect-able through the use of nifty snaps. The design from the outside is meant to resemble any normal socks and be invisible companion to the user. It is our belief that this philosophy will increase retention of the user.

Step 10: Files:

Hi , Kindly vote my project for the Instructables IOT and Explore Science Contests . Thanks !!

Invention Challenge 2017

Participated in the
Invention Challenge 2017

Explore Science Contest 2017

Participated in the
Explore Science Contest 2017

Internet of Things Contest 2017

Participated in the
Internet of Things Contest 2017