The modern smartphone is equipped with dozens of sensors that make them capable of capturing vast quantities of data. Many of the use cases for this data have revolved around tracking activity and mobility, but employers are taking a growing interest in monitoring the activity of employees, both through smartphone apps and custom devices workers wear throughout the day.
New research from Dartmouth highlights how effective this data can be at not only tracking employee performance, but enabling managers to judge employee performance. The system monitors the physical, emotional and behavioral wellbeing of the workforce, allowing and produces a classification based upon performance.
“This is a radically new approach to evaluating workplace performance using passive sensing data from phones and wearables,” the researchers explain. “Mobile sensing and machine learning might be the key to unlocking the best from every employee.”
Activity tracking
The system requires the smartphone to track things like physical activity, location, ambient light and the actual usage of the phone itself. This is augmented by data from a wearable fitness tracker, which captures everything from sleep levels to stress, weight to heart function. Finally, data from location beacons placed in the home and office provide information on where the individual is at any moment, how often they take breaks and so on.
The researchers have form, as they had previously created an app called StudentLife, which tracked student behavior, and claimed to be able to predict their academic performance. Their latest tool takes the data from the array of devices and processes it in a machine learning algorithm that has been trained to classify performance levels of workers.
“This is the beginning step toward boosting performance through passive sensing and machine learning. The approach opens the way to new forms of feedback to workers to provide week-by-week or quarter-by-quarter guidance on how they are approaching their work,” the researchers explain.
The system itself was tested by assessing a number of supervisors and non-supervisors from across a range of different industries, with their data compared with self-reported behaviors provided by each volunteer. Their performance was classified by things such as time spent at work, their quality of sleep and level of physical activity.
High performers
The data showed that the best performers would usually access their phone the least frequently, whilst having longer periods of deep sleep. They were also more physically active than normal, with high mobility levels as they visit a number of distinctive places during their work day.
It sounds like a dystopian level of surveillance, but the researchers believe it could actually provide valuable feedback to both worker and employer alike, and help to raise performance across the workforce. What’s more, the passive nature of the monitoring gives it the edge over existing review and performance appraisal techniques that are cumbersome and potentially biased.
“Passive sensors, which are the heart of the mobile sensing system used in this research, promise to replace the surveys that have long been the primary source of data to identify key correlates of high and low performers,” the team explain.
With the system able to distinguish between high and low performers with an accuracy of around 80%, the team believe it could allow traditional performance appraisals to be consigned to the dustbin. They hope that it will ultimately provide a more objective measure of performance at work.
“The passive monitoring system is meant to be empowering. This approach could certainly benefit companies, but can also be helpful to individual employees who are looking to boost their performance,” they conclude.
Whilst the system is not yet available in the app stores or the wider market, the team are confident that it will be available to managers and employees in the next few years.
