One of the most severe dangers for the elderly is falls. One unwanted fall may cause broken bones, a prolonged healing process, or even loss of independence. What is even more alarming is that a large ...

MyNotifi®, an automatic fall-detection wearable device, is a new system from MedHab that connects to a user’s smartphone and can send alerts. The system is a discrete device worn on the wrist and can ...

When every second counts, fall-detection technology can be the difference between a close call and a crisis. Today’s top-rated medical alert systems with fall detection blend smart sensors, intuitive ...

Falls are the number one cause of injury among adults 65 and older. But the truth is, your risk doesn't suddenly appear the day you turn 65. It increases gradually over time, especially if you're ...

Jessica is a former writer and editor at Forbes Health with over a decade of experience in both lifestyle and clinical health topics. Before Forbes Health, Jessica was an editor for Healthline Media, ...

The MarketWatch News Department was not involved in the creation of this content. Wearable Fall Detector Market Set to Reach USD 3.7 Billion by 2035, Driven by Aging Population and AI-Enabled ...

San Diego-based GreatCall, a seller of aging in place technologies purchased by Best Buy last year, is launching an updated version of its retail personal emergency response system. Called the Lively ...

A new study pinpointed seven factors that influence older adults and their families to use fall detection technology, according to a report published Tuesday in BMC Geriatrics. The team conducted ...

Most of us wish our parents would live as long as humanly possible, but some challenges arise if our wishes come true. While every individual is different, advanced aging usually brings about mobility ...

Fall Detection uses the Apple Watch’s built-in accelerometer and gyroscope to detect a hard fall. It initially responds by tapping you on the wrist and displaying an alert, offering you the options of ...

In a study published in the journal Information Systems Research, Texas Tech University's Shuo Yu and his collaborators developed a generative machine learning model to detect instability before a ...