Source: “PocketTouch: Through-Fabric Capacitive Touch Input”
T. Scott Saponas et al.
Proceedings of the 24th ACM Symposium on User Interface Software and Technology, Santa Barbara, California, October 16–19, 2011
Results: Researchers at Microsoft created a touch screen that can be operated through fabric. The system, dubbed PocketTouch, was incorporated into a prototype mobile device that could be operated while still in a user’s pocket. If a person’s phone rang during a meeting, a rapid touch gesture could silence it or send a particular text message in response. The system worked through 23 different fabric types, even the thick fleece of a winter jacket.
Why it matters: Although touch screens have become the default mode of interaction for mobile devices, they have drawbacks compared with traditional buttons. A conventional touch screen rejects any signal it detects that is not strong enough to have come from direct contact, which is why it can be frustrating to try to use one while wearing gloves. The Microsoft work shows that improvements to touch-screen technology could replace the lost functions left behind with traditional buttons.
Methods: The PocketTouch prototype was built by mounting an off-the-shelf touch sensor to the back of a smart phone’s case. Unlike existing touch screens, it has specialized software that can tune the sensitivity of the sensor to compensate for the thickness of the fabric covering it, helping it detect a clear signal through the fabric.
Next steps: The researchers will refine their prototype after testing it against different types of fabric. For practical reasons, the device also needs a way to detect automatically when it should shift between direct-contact mode and responding to touches through fabric (the prototype is always in fabric mode). It might do that by tracking ambient light levels, which most smart phones already do to tune display brightness. Another possibility would make use of new kinds of optical sensors.
Surveillance with in-home sensors can track a person’s mental state
Source: “Empath: A Continuous Remote Emotional Health Monitoring System for Depressive Illness”
Robert Dickerson et al.
Wireless Health 2011 Conference, San Diego, California, October 10–13, 2011
Results: University of Virginia researchers developed a combination of in-home sensors and cloud software designed to detect signs that someone is displaying behavior consistent with depression. One home was outfitted with sensors that report on the user’s sleep quality and body weight. It also registered when the person was at home and tracked movement inside the house. Software can combine the data gathered by those sensors into a “depression index” that could signal to caregivers when a person may need help to prevent or overcome an episode of depression.
Why it matters: According to the World Health Organization, depression affects about 121 million people worldwide and often goes undiagnosed. Using sensors to monitor at-risk people for early signs of depression, such as poor sleep and less time spent going out or on housework, cooking, and personal hygiene, could lead to much earlier detection and treatment. This approach is well suited to the care of elderly people who live alone; it is estimated that more than 15 percent of people over 65 have symptoms of depression. The researchers suggest that their design might also help soldiers returning from combat, who are at risk for post-traumatic stress disorder. While wireless health-tracking technology is increasingly common, until now it has been used only to record direct physical attributes such as heart rate, not to follow a person’s mental state.
Methods: The sensors took only an hour to install in a patient’s home and cost less than $800. The researchers installed wireless motion sensors on the bed to monitor movements during sleep, Wi-Fi-capable scales that track body weight, and sensors on doors and in rooms to track movement. An app for the patient’s cell phone periodically asked questions about mood and tracked voice characteristics. All this information was transmitted over the Internet to software that calculates a depression index and can also provide more detailed information to caregivers.
Next steps: The researchers plan to refine the system so that it tracks social interactions. They also intend to add a component that can monitor a person’s speech over time for features related to mood, and trials on more people at risk for depression are planned as well.
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