As people age, things change, and one of the major worries for seniors is falling. Many older people wish to stay independent and live on their own for as long as they can. But a fall can change ...

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 ...

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 ...

MedHab has announced the availability of a new wearable device that helps families and caregivers detect falls. Called the “MyNotifi Clip,” the product can be worn by residents either on their wrist ...

Lively offers several different medical alert products. You are not limited to an emergency pendant or necklace. Options include smartphones with emergency alert apps, a mobile alert device and a ...

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 ...

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 ...

The latest upgrade to AltumView's Sentinare sensor can eliminate false alarms, and solve a long-standing challenge for the senior care industry. A follow-up version will be released soon, which will ...

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 ...

Integration creates the industry's first fully unified view of fall events across entire senior living communities. FORT LAUDERDALE, Fla., Nov. 3, 2025 /PRNewswire/ -- CarePredict, the AI-powered ...

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 ...