Safety & Security – nami

What Is the Cost of False Alarms?

Jerome Leroy — Fri, 08 Jul 2022 00:43:00 +0000

Key Takeaways

False alarms are a major drain on society, and can be expensive for home owners
False alarms are problematic in all sensing contexts, including home security, health and wellness and home energy efficiency
Sensing 2.0 can reduce false alarms via smart system set-up and technology (such as WiFi sensing) specifically designed to eliminate most false alarms.

False alarms from home and building security systems, are both a public and private problem: They are a public problem as they are a significant drain on public resources. The Center for Problem-Oriented Policing conducted a study estimating that false alarms account for 94-98% of all alarm calls, with an estimated annual cost to emergency services of $1.8 billion. It is also now a private problem as, emergency services and municipalities rightly try to move the cost on to homeowners. For those seeking to explore more about this theme, it is highly recommended to learn about facharbeitschreibenlassen.com. In this article, we look at the cost of false alarms, their causes, and what can be done to try and prevent false alarms in security and sensing systems.

What is the definition of a false alarm?

A false alarm occurs in the security context, whenever a security alarm is triggered by an event other than the expected trigger event. In some ways, the term ‘false alarm’ is less-than-ideal as it implies something incorrect or lacking in the alarm itself. For example, if I arrive home setting off an alarm, having forgotten to disarm my home security system, the result is a ‘false alarm’. But the home security system worked entirely accurately, and responded to the correct trigger event. For this reason, ‘false alarms’ are sometimes labeled ‘nuisance alarms’ to emphasize that it is the response to the trigger event, rather than the trigger event itself which is the problem.

Do false alarms only relate to security?

Most commonly we may think of false alarms as false security alerts. But false alarms commonly arise in other contexts as well. In fact, false alarms are a problem for any ‘sensing technology’: That is, a technology that detects physical or biological phenomena and transforms it into electric signals for eventual interpretation or response.

False alarms are a particularly acute problem in the clinical healthcare context with monitoring equipment, such as electrocardiograms (ECGs): In one emergency department study, only 1 percent of alarms were found to be clinically actionable, requiring intervention.

In the consumer HealthTech industry, false alarms can occur where wearables and other devices indicate a serious health problem, where there isn’t one. For example, smart watches configured for fall detection have triggered false alarms on ski fields where falls are commonplace.

Arguably, the societal cost of healthcare false alarms may be even more significant than in the security context: Healthcare emergency services mean life and death, and the opportunity cost of responding to a false alarm could be someone else dying.

On a more mundane, day-to-day level, false alarms in sensing technologies can be a nuisance within the home: For example, if HVAC systems are set up to turn on whenever an individual enters the room, but it turns on whenever the robot vacuum cleaner is in operation, potential energy savings can be thwarted.

What are the costs of false alarms?

The costs of false alarms depend on the set up of the sensing or motion detection system: If the system is set up to automatically contact emergency services, it will obviously be more expensive than a system which is not set up in that way. With that in mind, some of the most prominent costs include:

Costs of any dispatched security guards/emergency personnel
The costs of call takers and dispatchers
The indirect HR and equipment costs associated with response staff (for example, maintenance and depreciation on emergency vehicles)
The opportunity cost of time/resources that could be spent responding to real trigger events
The productivity loss from ‘switching’ or ‘multi-tasking’ in order to respond to a false alarm event, disrupting focus
The ‘Boy who cried wolf’ effect on individuals who become so accustomed to false alarms, they develop ‘alarm fatigue‘, and don’t pay attention to genuine alarms.

What are the causes of false alarms?

False alarms have a range of causes. Some of the most common ones are:

Incorrect placement of sensors, for example, motion detection sensors located next to heating ducts or fans
Oversensitive motion sensors, which pick up the motion of pets, robot vacuum cleaners, and other minor movements, rather than simply picking up human motion. This is often a result of using outdated sensing technology
Poor wiring, such as where a hardwired security system is installed and the wiring has become degraded over time
Lack of maintenance, such as where batteries are not replaced regularly in devices that require them
Human error, where users have failed to adjust sensitivity settings to ensure that only the right trigger events set off an alarm
Poor system design, where there is no ‘review step’, requiring confirmation before emergency services or security guards are dispatched. For example, it is common for system monitoring companies to phone a property and check whether assistance is needed before physically sending someone out to the property.

What is the best way of reducing false alarms?

Some ways of reducing false alarms are common sense: Ensuring that systems are well-maintained, and batteries are replaced regularly etc. However, the best mechanism for reducing false alarms lies in the design of the system itself.

Ideally, a system should:

Be easy to install and adjust — where the location of the sensors is causing false alarms, they can be easily moved. Straightforward installation, such as ‘plug and play’ units that connect directly to power outlets reduce the risk of ‘hardwired errors’
Have filtering mechanisms to filter out common sources of false alarms, such as pets or wind. How this is done depends on the sensing technology in question. For example, Passive Infrared (PIR) sensors can be configured to only recognize temperature changes caused by sufficiently large objects (i.e., humans, not animals). Other types of sensors, such as AI-enabled video cameras, radar sensing and WiFi sensing can also apply algorithms to do this (more on this below).
Be ‘dummy proof’, reducing the risk of error due to forgotten or mistyped security codes, for example. For example, some security systems are fitted with geolocation mechanisms that automatically identify when certain people have arrived home via a signal on their phone.

How sensing 2.0 reduces false alarms

Traditional motion detection technologies can be seen as ‘Sensing 1.0’: Analog sensors pick up movement, which triggers certain events. But sensing 1.0 is unable to learn, which makes it prone to false alarms.

Sensing 2.0, means an intelligent sensing ecosystem:

Intelligent algorithms which learn over time to distinguish normal motion from abnormal/alarm-triggering motion
Integration with existing IoT and apps, enabling the user to respond and deal with any false alarms as they arrive and before authorities are contacted.
Wide and deep coverage — motion detection ‘without the gaps’. Sensing 2.0 means detecting motion through walls, and comprehensively across regions of the home. There is no reliance on piece-meal sensors.

A common component of Sensing 2.0 is WiFi sensing: Using the power of wireless local area networks (WLAN)— to detect and interpret presence and motion. Motion interrupts WiFi waves in predictable ways, allowing the alarm to be raised.

Wi-Fi sensing eliminates false alarms through two stages:

Pre-processing and initial filtering. The raw channel state information (CSI) provided by WiFi devices is filtered to rule out pets and other non-human movements
Higher-level processing. AI and machine learning algorithms are applied to that initially filtered data. This allows for sophisticated use cases, such as analyzing breathing patterns and repetitive movements. This is particularly useful in reducing false alarms in a health and wellness context: Traditional medical alert devices can be accidentally pressed by the user, whereas WiFi sensing can be used to automatically alert family members or emergency services where motion patterns show a fall, or problematic breathing.

Conclusion

A major focus of all sensing and motion detection systems should be to reduce the proportion of false alarms. False alarms have a range of causes, two of the more significant ones being human error and the use of error-prone sensing 1.0 technology.

The best way to reduce false alarms is to develop a multi-pronged approach:

Train all users in how to use the sensing technology correctly
Choose a technology and sensing ecosystem (such as ‘Sensing 2.0) that is configured to reduce false positives
Ensure that the system is installed as intended.

FAQ

What causes false alarms?

There are many events that can cause false alarms: Poor system maintenance, poor design, and human error, to name some of the most common causes.

`What is an example of a false alarm?

A false alarm occurs, for example, when a pet cat sets off the intruder alarm and alerts a connected security service to a potential break-in.

What happens if there is a false alarm?

It depends on the precise set up of the sensing system. For example, with some security systems, only the home owner will be notified (for example, via an app). In others, a third-party monitoring service will be notified and they will arrive at the home to investigate the disturbance.

How can I prevent false alarms?

Preventing false alarms is a combination of appropriate training for the user, technology which is primed to reduce false alarms (such as WiFi sensing), and a system design aimed at reducing errors.

How Digital Sensing Can Detect Fire Damage within the Home

Gleb Vodovozov — Tue, 01 Mar 2022 00:05:00 +0000

Key Takeaways

Fires cause enormous property damage and loss of life. In 2020, this meant $21.9 billion in property damage and 3,500 deaths in the US alone.
Digital sensing solutions help prevent and protect against fires through relaying smoke alarms to occupants, family members and emergency services.
In the future, it may be possible for WiFi sensing to detect smoke and fires directly.

Smart homes and buildings have a range of digital sensing technologies designed to make the home safer, healthier and more energy-efficient. Here we look at how digital sensing technologies can help detect smoke and fire damage within the home.

The Destructive Impact of Fire

The impact of fire damage in our homes and communities makes for some sobering statistics:

There were 490,500 structure fires in the United States in 2020. This is an increase of 1.9 percent on the prior year.
Fires caused 3,500 civilian deaths and15,200 civilian injuries in that year.
In the US alone, fires caused $21.9 billion in property damage in 2020.
Seventy-four percent of all fire deaths relate to fires in the home.

While smoke detectors are now, thankfully widespread, most home smoke detectors simply sound a loud alarm and do not communicate or transmit that alarm any further.

This means that smoke and fire detectors alone offer limited protection:

They do not notify you that the smoke alarm has been triggered when you are outside the home
They do not automatically notify other individuals elsewhere, such as family members or emergency services, when the alarm has been triggered
They only offer one, or perhaps two, channels indicating the alarm has been triggered (sound and light).

This means that a traditional smoke detector set-up is particularly deficient when it comes to protecting vulnerable family members (who may not hear the alarm), or preventing property damage (where no one is around to hear the alarm).

In this article, we explore how digital sensing systems, by integrating traditional smoke and CO alarms into smart home infrastructure, can save lives and properties: Smoke and CO alarms can be relayed to welfare apps on smart devices, providing additional warning (through sound and vibration), and communicating the alarm being triggered to those outside the home.

What is the Definition of Digital Sensing?

So, what is digital sensing? First, it is necessary to establish what sensing in general is: Sensing means any kind of technology that detects physical or biological phenomena, and translates that information into a signal: That could be a digital signal, but it may not be.

For example, an old-fashioned mercury thermometer is a sensor: The mercury responds to physical phenomena (heat), and translates that into a visual representation of temperature (the gauge on the thermometer). Thought of in that way, sensing goes back thousands of years. A sundial is a sensor — translating the visible light energy of the sun into a representation of the time of day.

Analog signals, mean that the sensor produces a continuous, variable, signal, which is parallel to the measured value. For example, thermometers produce an analog signal (the reading), which is parallel to the change in the mercury.
Digital signals, mean that the sensor produces just two discrete states, which may be described variously as ON/OFF, True/False, or 0/1. For example, a Passive Infrared (PIR) motion sensor produces a digital signal: Its state is ON and the sensor triggered, when a certain fluctuation in heat energy is detected. Otherwise, it is in its OFF state.

In modern sensing, the concept of a sensor evolves into something that translates physical or biological phenomena into an electrical signal: This includes motion detectors, sound detectors, and smoke and CO detectors.

So, is sensing in the modern smart home, analog or digital? Often, it’s both. But ultimately the output of analog sensors is transmitted into digital signals in order to be interpreted by devices with microprocessors, hence the collective term ‘digital sensing’, for all the sensors in a smart home.

Digital Sensing and Fire Detection

Smoke detectors come in two main forms:

Photoelectric, or optical smoke detectors, which contain a source of light, such as a light-emitting diode (LED) and a photo-receiver (a component that detects the light from the source): These components are arranged in the casing of the smoke detector, around which, air flows. The light received by the photoreceiver reduces in intensity when smoke particles are in the air, triggering the alarm when a certain threshold is met.
Ionization smoke detectors involve a radioisotope ‘ionizing’ the air. In essence, ions attach to smoke particles, altering the electric current within the unit and triggering the alarm. Ionization detectors are usually cheaper to produce, but are less likely to give early warning of a fire relative to an optical detector.

Distinct from smoke detectors are carbon monoxide (CO) detectors. CO is the odorless, colorless, but lethal, gas produced by incomplete combustion. In their most popular form, CO detectors work by use of an electrochemical cell, precisely calibrated to recognize when the CO levels have increased to a dangerous threshold (through a chemical reaction where CO is oxidized into CO2).

In an ideal smart home set-up, all sensors crucial to the safety and wellbeing of occupants, such as CO and smoke detectors should be integrated into a unified alarm system, such as alerts via an app.

While it is possible to hardwire smoke detectors and CO detectors into such a system, this requires expensive and time-consuming installation and alteration works. A simpler way to integrate these detectors into a digital sensing system is via simple intermediary sensors: A separate audio/acoustic sensor calibrated specifically to the frequency of a smoke or CO alarm is a useful way of integrating those alarms into a digital sensing system.

Once smoke and CO detectors are integrated into a digital sensing set-up in this way:

Occupants in the home will be alerted to the smoke detector via sound/vibration on their smart device — crucial for occupants who are hard of hearing, or wearing headphones
Designated family members, neighbors, or friends outside the home, can be immediately informed that a smoke or CO alarm has sounded
Emergency/fire services can be automatically notified and dispatched.

Can WiFi Sensing Aid in Fire Detection?

WiFi sensing is a form of motion detection that picks up disturbances in the electromagnetic radiation emitted from WiFi. It is an accurate and cost-effective sensing technology for detecting intruders, detecting falls, and ensuring energy-efficient appliances respond appropriately to movement.

In principle, fire can respond to electromagnetic fields (due to an ionization process), altering the transmission of WiFi signals: More specifically, WiFi sensing utilizes ‘Channel State Information’ or ‘CSI’, which describes how a signal is affected over the distance between a transmitter and a receiver. Some early research suggests that fire affects both the amplitude of wireless signals and their phases, and that this can be accurately picked up by WiFi sensing technology providing early detection of fire.

However, at this stage, WiFi sensing has not been shown to more reliably indicate a fire than traditional smoke detectors: It is quite possible that smoke detectors are better at picking up the fire earlier at the ‘smouldering’ stage, before visible flames are present. However, it is a promising line of research, and at some point, WiFi sensing may be able to detect smoke and fires as accurately as other sensors.

Digital Sensing is Integral to Smart Fire Detection Systems

With climate change-related fire risk on the rise, accurate and early smoke detection should be a key part of every individual’s smart home or smart building infrastructure. Digital sensing solutions make it easy to relay smoke and CO alarms to individuals inside and outside the home: This will prevent some fires, mitigate the damage of other fires, and save lives.

FAQ

Are smoke detectors analog or digital technology?

Strictly speaking (whether photoelectric or ionization), smoke detectors produce a digital signal, as the output is either ON or OFF. In a more colloquial use of the term ‘digital’, smoke detectors become digital when their output is interpreted and communicated by smart devices containing microprocessors.

What is an example of digital sensing technology?

Acoustic or audio sensors which pick up the frequency of breaking glass (as an intruder alert) or smoke detectors (as a relaying device), are examples of digital sensors.