Adelego is a project created to help dependent people live longer and more safely at home & to improve the assistance caregivers can provide.
A person is dependent if he/she is reliant on someone else to accomplish or complete common tasks, such as cooking meals. People become dependent because of their age, a disability and/or an illness. Dependence can last between a few days (e.g. after a surgery) and a lifetime (e.g. a person born with a mental disability).
A caregiver is a person who helps dependent people in their daily life. A caregiver can be a nurse, a family member or even a neighbor. In most cases, a caregiver is the spouse or a parent of a dependent person.
If an accident happens to dependent people, it can take a long time before a caregiver is alerted and helps them out. Unfortunately, the longer it takes to help dependent people after an accident, the more their general health deteriorates.
The population in countries across Europe and the Americas is aging leading to an increase in the number of dependent people and consequently, caregivers. Today, it is estimated that about 1 in 5 people is a caregiver in Europe and North America and this number is expected to increase for the next few decades due to the aging of baby boomers. Unfortunately, this situation is not economically viable due to the increasing proportion of dependent people, the overall impoverishment of the population as well as the high rate of health care costs. To solve this societal issue, we want to develop solutions that are simple, robust, easy to use and affordable for dependent people and caregivers.
We are turning to the use of connected objects and the implementation of digital applications to develop a personalized local home surveillance system that allows:
- people who are living by themselves and/or dependent to live safer and longer in their own home.
- caregivers to be alerted immediately in case of an accident.
Because each situation is unique and changes over time, our approach is to create a platform that will provide a variety of tools users can select based on their needs. It is the combination of these tools that will create the personalized local home surveillance system. Each tool will consist of a program in link with connected objects to:
- record and analyze data locally;
- send an alert (by phone or email) to a caregiver when an unusual activity is detected, such as a fall.
We are currently building a first prototype consisting of a detector connected to a Raspberry Pi 4 on which we installed Jeedom.
The detector we are working with is Multisensor 6 from Aeotec. This detector can sense humidity, temperature, presence, UV, brightness and vibration. It uses the Z-wave protocol to communicate to its antenna (Z-stick Gen 5+ from Aeotec) that is hooked up to the Raspberry Pi 4.
Raspberry Pi 4 and the antenna (left) and Multisensor 6 detector (right)
Jeedom is an open-source software that is designed to create a personalized home automation system. We are currently using Jeedom to collect and export our data as CSV files.
The detector has been placed at the entrance of our kitchen since December 1st 2020 and has been recording data continuously.
The appropriate data analysis is essential to detect any unusual activity. Our current strategy is to use the Python library, pandas, to analyze the data that has been recovered in CSV files.
We first did an analysis of the data over a period of 3 months from January 1st 2021 until March 31st 2021 in order to get a history and figure out a pattern. Information we obtained from this analysis:
- What time do we enter the kitchen for the first time of the day?
- How much time do we spend in the kitchen per day?
For both questions, the goal was to provide the mean, standard deviation and a graph. Below are the graphs summarizing our analysis. On both graphs, the red line represents the average and the green lines are located 1 standard deviation away from the average.
With this analysis, we then defined that any data located between the two standard deviation lines were considered "normal" while any data that would be outside of these lines would be subjected to concern.
The next step of our analysis is to automatically analyze incoming data, compare the results to the history generated with the 3 months of data and send a notification via email. With Jeedom's plugins, we programmed our system to receive an email at midnight everyday containing a CSV file with the data from the last 24 hours. We wrote code that provides the first time we enter the kitchen and the time spent in the kitchen per day for each incoming file. This analysis was started on April 1st 2021. The picture below is a screenshot of the dataframe generated on April 21st 2021. This table includes the following columns:
- First time: time when we entered the kitchen for the first time of the day
- First time range: range indicating if the First time is in the normal range or if it is early or late compared to the history*
- Time spent: time we spent in the kitchen for the day
- Time spent range: range indicated if the Time spent is in the normal range or if it is low or high compared to the history*
*Note: the normal range is any value that falls within 1 standard deviation from the mean calculated with the 3 months of data.
If you want to develop this project with us or have ideas/suggestions, feel free to contact me on GitHub.
We also created a GitHub page with more information on the project and our vision.