For decades, we’ve heard the promise that computers are going to make work easier. It seems only now that the Fourth Industrial Revolution, or Industry 4.0, is starting to deliver on that promise through stark, disruptive technological transformation. While many things are significantly easier – for example, who wants to roll back to queuing in a bank or posting a cheque rather than a 20-second electronic funds transfer? – I look around my own and my customers’ industries, and it appears for many, that work is perversely becoming harder. Put another way, work is becoming more pervasive in our lives.
That’s because, in my observation, while the time it takes to execute tasks and processes is reduced dramatically, conversely, the number of tasks and processes to be executed in each day is increasing. We are chasing our tail; as each task becomes faster, we fill the time we save with more tasks. Perhaps we are on an uphill trajectory of a graph with an x-axis of busy-ness and a y-access of digital advancement. I’d like to think the rise is not a flat trajectory and that we’re about to ride over a peak and head downwards.
The downhill ride will be when simplification outstrips our work environments’ capacity to bombard us with more to do. I suggest we are nearly there, but in the meantime, the increased volume of tasks is a common source of workplace stress. Perhaps we have progressively swapped out the physical safety risks that peaked at the time of the first Industrial Revolution with mental safety risks associated with modern, complex lives inundated by information.
I believe we’re striving for a balance in which we are working smarter and being more productive and efficient, but not at the cost of our health (and let’s face it, hours stuck at a desk has a terrible impact on our physical as well as mental health).
While I would suggest that dramatic simplification will get us onto the downhill trajectory, I see the same technologies that will deliver that simplification, such as artificial intelligence, machine learning, and predictive analytics, are hinting at other ways to better manage our health and safety.
One of the more fascinating remits of my own work is the exploration of how technology can improve human performance and safety. For example, ensuring availability of athletes and soldiers through injury risk prediction, or the concept of connected workers—i.e., making the soldier, the police officer, and the firefighters a tangible part of the organization’s common operating picture.
Bringing human performance and safety into the mix can extend situational awareness beyond the knowledge of assets and equipment and a person’s geo-location and skillset. If we can understand a person’s physical and mental well-being in the context of their prospective or current mission, we enable operational decision-makers to truly ensure best “troops to task” or, to keep with the military vernacular, when to pull those troops from the frontline.
I have seen that predictive analytics applied to medical records and athlete well-being self-assessments offer the means to predict injury. Meanwhile, rapid advances in wearable sensor technologies provide for the collection of physiological (e.g., heart rate) and biomechanical (e.g., gait) data. Pioneers in behavioral authentication are using such data to bring us to a point where an understanding of a person’s normal range of motion – their inertial signature – can provide a factor of authentication. Pivot this to human performance, and we could assess when someone is not moving freely; e.g., as an indicator of fatigue or identification of the emergence of an injury.
Overlay physiological and biomechanical data with workplace data (hours worked, tasks conducted, welfare records, etc.) and run advanced analytics incorporating machine learning, and we could start appreciating when people are reaching a point of physical or mental harm. Or at least we could have systems flag probability of harm for a human to decide whether to intervene.
- A soldier’s sub-optimal inertial signature on a route-march combined with recent medical history suggests a high likelihood of injury
- A prediction of injury risk to an athlete is reinforced by an inertial signature indicating rapid onset of fatigue
- After two night shifts involving an unusually high exposure to people experiencing traumatic events, an emergency response center worker’s physical demeanor suggests a third night could be harmful
- A bush firefighter’s physiological data, combined with geo-proximity to colleagues on task and environmental conditions (e.g. temperature from worn sensors or smoke visibility from streaming on-person video footage), suggests an immediate intervention to withdraw him/her to safety
- A police officer’s stress levels coupled with exposure to an abnormally high number of violent confrontations in the last few shifts trigger a welfare/mental health alert.
I’m personally fascinated and excited by such prospects for technology over and above the promise of working easier, or at least smarter. The prospects for safety, of course, go well beyond my own focus on defence, public security, and sport. I’d love to hear your thoughts on what might be possible in this space in the very near future.
For more on this topic, see The Human Side Of Machine Learning.