Body Weight Readings using Machine Learning on IoT Data
  • The Results

Body Weight Readings using Machine Learning on IoT Data

Challenge completed

More than 50 technology changemakers worked with proprietary, anonymized, HIPAA compliant* Smart Home sensor data. The team developed a Machine Learning model that can infer true body weight from an IoT connected scale and assist with the remote sensing of elderly and at-risk patients, improving their overall care with minimal intervention.

 

The partner EmPowerYu provides resident-authorized formal and informal caregivers with non-intrusive means to be assured of the wellbeing of care recipients and to be alerted in exceptional conditions. Existing signals include quality and time of meals, quality and time of sleep, and general activity of the house including departure and return.

 

The Problem

Remote monitoring with EmPowerYu helps people at high medical risk live more safely in the community. Most remote monitoring requires the resident to actively engage with smartphones, but older adults and disabled people often have physical, cognitive, technical, economic, or language barriers that make smartphone systems impractical. EmPowerYu works for everyone. EmPowerYu’s Smart Home system is effortless for the resident, so we collect complete and long-term whole-person datasets, unlike wearable sensors that people stop using after a while.

Effective remote monitoring reduces healthcare utilization, improves care decisions, and lowers stress for caregivers. True Body Weight is the actual weight of the body tissues. It is the ‘first thing in the morning’ weight. E.g. (after rising from bed, before dressing in day clothing, after first toileting, before eating or drinking).

True Body Weight changes over an interval of days (for example, one week) can indicate significant changes in wellbeing, such as a worsening of heart disease or decline in appetite, that would benefit from intervention.

A five-pound change of real body weight is clinically significant. Detection is confounded because weight over the course of a day can vary by five pounds because of daily activities that are not clinically meaningful, like the weight of clothing. Reliable adjustment of a weight measurement to real body weight under standard conditions would permit a more accurate and earlier determination of concern.

Excess alarms to care recipients cause alert fatigue and must be avoided to assure timely response and product success.

 

The Solution

Within two months the team created a solution to clean and normalize biomarker and sensor data provided in various unstructured formats. From that the team engineered 13 predictive features of true body weight, trained and evaluated 20 different machine learning models, deployed the model as a Flask app with an interactive dashboard, created a Flask API with the model productionized and communicable via REST endpoints, and developed a fully coded pipeline for a REST API capable of normalizing the disaggregated sensor data in real-time from inception to inference.

 

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