In recent years, Kendal and Crosslands have increased their emphasis on fall prevention. That makes sense: the CDC reports that falls are the leading cause of both fatal and non-fatal injuries among people over 65. Our fitness and balance classes are a big help, and our medical staff is careful about prescriptions that could lead to unsteadiness. Our PT department helps people determine if they need walkers or canes.
And then there are the electronic devices that can help. I imagine all readers of this blog are familiar with the pendants and watches that are often used to help detect falls and to summon aid in case of a fall. Those are widely used and important, of course, but now a spectrum of new technologies is becoming available that can potentially improve on those methods.
Many of these new approaches were on display in the exposition hall at LeadingAge’s recent national convention which I was able to attend. They involve use of various combinations of video, audio, and radar, combined with artificial intelligence (AI) to detect and respond to falls or the potential for falls. Many of these products were pitched as tools that could pay for themselves by reducing the staff time involved in night rounds in assisted living settings.
Radar? Really? You may be surprised to see radar on the list of technologies used. I certainly was. But low-power radar has become common in the automotive industry (for crash avoidance) and the technology has now made it into the healthcare field. It turns out that radar can not only help follow a resident’s path within their room, but (with analysis help from artificial intelligence) it can even detect the subtle body movements of breathing and blood flow.
The devices and systems. The rest of this post lists some of the new technologies that are being used either to reduce the likelihood of falls or to improve the speed of response to a fall. In many cases, the same technology helps with the detection of other health problems in addition to falls. The devices generally connect to the local WiFi system (like the ones we have in the Kendal and Crosslands Centers) and offer a web-based dashboard. In many cases, I was able to get rough pricing for the systems. Many of the products listed below are new and lack a track record, so caveat emptor.
Here, in no particular order, are the monitoring technologies (and their vendors) that caught my eye at the LeadingAge event.
The Paul system from Helpany (https://helpany.com/). You install the “Paul” unit (radar) on the ceiling of each room. It identifies residents who are a fall risk by analyzing “activity levels, nighttime restlessness, frequent bathroom trips, and other behavioral changes”. Emergency calls go right to the caregiver’s phone; more routine information is recorded on a dashboard. In small numbers, the cost is $650/unit, $24/month. Bulk purchases are less. Helpany also offers pendants and watches that feed into the same system.
The Sensi Pod from Sensi.ai (www.sensi.ai). This is a listening device (audio) that can detect distress sounds during emergencies, including falls. By tracking the frequency of bathroom trips, it can also catch signs of urinary tract infections. The company claims “22% reduced hospitalizations” due to early intervention. It sends out phone alerts and presents data on a web-based dashboard. The audio-detecting units plug directly into a wall socket (“like air freshener”) and cost $90-120 each. Typically, 2 or 3 will be deployed around the living space.
Sleepiz unit from Sleepiz (www.us.sleepiz.com). This is a radar wall unit that is installed above the bed. It is not really for fall prevention but could be useful in that context. It is basically intended to record information about sleep patterns. In addition to the person’s location (in bed/not in bed), it is sensitive enough to track heart rate, breathing rate, and whether the person is awake or sleeping. The cost is $200/unit plus $30/month.
FallSight Predictive Solution from Thrivewell (https://thrivewellTech.com). This is a software package, not a device. It analyzes a community’s electronic health records to identify people who might be at risk for a fall because of their medical history and medications. Thrivewell claims the early-user communities using its software have been able to reduce falls by 21%. The pilot version of the software costs $1500. The price will decline after roll-out.
Havn Sensor from havn (www.havnwellness.com). Another brand-new device. This one combines audio and radar. It tracks “presence, room occupancy, posture, body position, … activity levels, standing/sitting time, and sleep patterns”. Since it has a speaker as well as a microphone, a caregiver can call into the room and initiate contact. The unit can provide medication reminders, and can enter into an AI-based conversation. Since the system was just announced, the pricing may not be final. But it is likely to be around $199/unit and $65/month.
MyRaina (https://www.myraina.com/). This device uses a wall-mounted clock with a built-in sensor (I did not learn what type of sensor it is) “to detect falls and anticipate fall risks”. It “monitors movement and posture without capturing videos, photos, or audio recordings”. It sends alerts to staff if a fall or fall risk is detected.
Vinci Vision (www.vincivision.ai). This system uses video to detect falls and unusual behavior, such as aggression. The software analyzes the video feed to detect and report situations that require a visit by the staff. But the system does not store the video. Instead, it uses AI to create a stick-figure representation of the person, based on the video, and that is what is stored and provided to the nursing staff. That process is intended to protect the person’s privacy while still letting staff see the circumstances of a fall or other problem. This is a subscription service that costs $2000/mo.
NightWatch from NightWatch Robotics (https://www.nightwatchrobotics.co/). NightWatch is a promising new product, but it has not yet made it out into the field. Here’s the concept: each night, a radar-equipped robot patrols the halls of an assisted-living facility, using radar to check on the occupant of each room. Without entering the room, the robot can determine via radar images whether or not the occupant is in bed and whether they have fallen and are on the floor. If it works as advertised, this approach could save a lot of nurse’s-aid time, and it would be much less intrusive than knocking on doors and potentially waking the occupant. The company has developed a prototype and is now looking for “two or three communities to help shape NightWatch”. The company is not ready to set pricing, but the robot would presumably pay for itself in staff savings.
