“I’m hopeful this passive sensing technology will be used to empower the workforce rather than used against them,” says Campbell. They suggest employers could offer incentives to encourage workers to opt into the system or that the tools should remain in workers’ hands. The researchers acknowledge the potential for volatility if data from the system is used by workers without their consent. “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,” says Pino Audia, a management professor at Dartmouth’s Tuck School of Business. “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.” “This is the beginning step toward boosting performance through passive sensing and machine learning,” Campbell says. The researchers also stress the technology’s usefulness in helping workers see the links between their own well-being and their ability to do their job. The study, which also made use of traditional questionnaires to categorize performance, found that higher-performing workers tended to use their phones less, had longer periods of deep sleep and were more physically active. Over one year, the researchers examined 750 employees – supervisors and non-supervisors in a range of industries – with their performance classified by factors including time spent in the workplace, their sleep quality and physical activity. It’s based on Student Life, an earlier app developed by Campbell to monitor the behavior of students and predict their academic performance. The information gathered from the three devices is integrated into an app called Phone Agent. “Mobile sensing and machine learning might be the key to unlocking the best from every employee.” “This is a radically new approach to evaluating workplace performance using passive sensing data from phones and wearables,” says Andrew Campbell, the Dartmouth computer science professor who led the research. They said it will offer both a complement and an alternative to existing performance measurement tools like evaluations, self-appraisals and interviews, which can be biased. They said it will allow technology to help workers optimize their performance while enabling companies to assess individual performance. The researchers focused on the benign aspects of the system. The data from the monitors is processed by cloud-based machine learning (ML) algorithms trained to classify workers by their level of performance. A location beam that track’s the time spent actually working.A wearable fitness monitor that tracks heart rate, sleep patterns, stress levels and caloric intake.A smartphone that tracks a worker’s physical activity, phone usage and a room’s ambient light.The perfected system will categorize workers with 80% accuracy, the researchers say, and requires three inputs: The system, announced by the lead researcher at Ivy League Dartmouth College in New Hampshire, is years from entering the workplace but has the potential to excite managers and scare workers. Researchers at nine American universities have developed a system of sensors that will eventually track workers in the office or at home and classify them as high- or low-performers.
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