Treating Addiction with IoT and AI

Patrick Bailey -
Illustration: © IoT For All

The interconnectivity fostered by IoT and the learning abilities of AI show potential in tackling a wide range of problems. Although not the first on our minds, substance addiction is one such problem IoT and AI can work against. This will completely revolutionize rehab and allow for the remote treatment of addiction during pandemics like these, where in-person treatment is less viable. Let’s look at some of the ways IoT and AI can treat addiction.

Habit Tracking

One of the marvelous traits of many IoT items and AI programs is their ability to track our habits. Through the creative use of sensor networks, a person’s addiction can be monitored.

The QuitBit is one such example. The QuitBit is a lighter that syncs to smartphones through Bluetooth. QuitBit tracks how often users light up cigarettes, displaying the number on its screen. In the app, QuitBit calculates trends in user habits, like what times of day they smoke, which days of the week they smoke, etc. There is no lighter fluid required, charging through an electric coil which can last for 100 lights.

IoT innovations such as QuitBit can make the numerical severity of addiction more obvious. Going through packs of cigarettes without any thought, one can easily lose sight of how frequently one smokes. But with QuitBit, that number is always looming over you on your lighter.

AI Responses to Habits

Cue operates similarly to QuitBit. Cue is an app individuals can add to a smartwatch. The app’s AI learning algorithms can detect when a user is smoking based on hand movement. Not only does Cue record smoking patterns, but it’s able to predict when the user will smoke next.

Preemptively, Cue will send texts to encourage users to delay their next cigarette or advise the user to do something else like play a game or go for a walk. Users earn points for increasing the time between each smoke which can be redeemed at Starbucks and Uber.

Not only does Cue track your habits, but the AI offers its own responses, actively trying to treat smoking addiction with distractions and a points-based system. This engaging approach offers greater incentives to knock addictive habits.

Pavlovian Conditioning

While Cue operates under a rewards-based approach, other IoT and AI-based technologies punish users for following through with addictive behaviors.

The Pavlok is a wristwatch that sends an electric shock when a user is about to cave in on their addictive habit. Like other IoT devices, Pavlok can connect to phones through Bluetooth. But the caveat is for some addictions, you have to deliver the shock yourself. For instance, if you’re addicted to alcohol and find yourself about to drink, you have to deliver the shock to yourself. Pavlok has the potential to work in these scenarios, but part of the success is reliant on a user’s willingness to hold themselves accountable.

Some addictions are associated with specific hand movements, as was discussed with smoking, but it applies to other addictions as well like nail-biting or hair pulling. Keen is another device worn on the wrist that can be trained to recognize bad habits and will automatically deliver a shock. Like Pavlok, it still relies on some degree of self-accountability (you still have to put on your bracelet), but AI recognition gives the device a greater degree of automation, shocking users even if they wouldn’t press the button.

IoT and AI technology can give us carrots and sticks to help us fight off the addiction without needing another human present to stop us. When we invite other devices into our IoT systems, their potential expands further.

Greater Interconnectivity Offers Even More Promises

A virtual breathalyzer is in development to combat alcoholism. Sensors in smartwatches, fitness bands, and virtual glasses are able to detect someone’s level of intoxication based on their walking patterns, noting the difference in one’s gait between walking to the bar and walking out of the bar. One of the developers is optimistic about the program’s future applicability, especially if it can be synced up to other devices. For instance, if the car is connected to the system, the car can be made to not start until after a designated period of time, or until the person demonstrates themselves to be sober.

One study proposes an IoT model where cars are equipped with sensors that can detect a person’s level of intoxication. In response, the car could turn off its ignition and send alert messages to friends or family. The study also proposes sensors that detect how frequently someone’s eyes are closed to infer their drowsiness. The car can respond with noises or vibrations to wake up the driver. Although not directly related to addiction, this leads one to wonder if advances in eye pattern recognition technology might allow programs to infer if a person is under the influence of drugs such as marijuana, which impact eye coloration and movement.

While not directly dealing with addiction, these IoT developments have clear potential to prevent addicted individuals from harm. We also can imagine how some of these IoT models can be applied in other ways as well. For instance, an IoT breathalyzer which detects one’s BAC based on the breath can keep track of their alcohol consumption, like how often they drink and what times. While the number of drinks one has consumed would be difficult to track, with one’s BAC deciphered, the IoT program could produce some standardized measurement: “based on your BAC, you have drunk the equivalent of 4 cans of beer today.” Clearly, IoT and AI have much potential in addiction treatment.

Conclusion

By being able to delicately monitor our addictive habits, IoT and AI-based devices can provide users with detailed analytics on how often they engage in our unwanted habit. Further, they can give us carrots and sticks as a way of treating our addiction, rewarding good behavior, and punishing bad behavior. Introducing other devices into the IoT system shows even more potential in being able to safeguard us from the consequences of our habits and showing us to what dangerous levels we take our addictions.

IoT and AI will dramatically change how addiction is treated, available to both individual consumers and clinics who offer them to patients. Imagine being able to attend alcohol rehab remotely, being given a virtual breathalyzer by the rehab center. Addiction treatment tools could become more commonplace, and more and more people would feel empowered to overcome their bad habits.

In spite of breakthroughs offered by IoT and AI, one constant remains: an individual’s willingness to change. These apps and programs can only nudge a person in the right direction, sending motivational texts or shocks. The patient has the final say on whether they can or cannot be treated and cured.

Author
Patrick Bailey - Writer

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Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All. If you're interested in contributing to IoT For All, cli...
Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All. If you're interested in contributing to IoT For All, cli...