![]() First, we used the seed model to sort through large amounts of data and extract infrequent events. To address this problem, we started by training a seed model on a relatively small amount of data. Reducing false-alarm rates is especially challenging because of the well-known long-tail problem in AI: there are a multitude of rare events that could fool a detector, but their rarity means that they’re usually underrepresented in training data. The biggest algorithmic challenge we faced was achieving high detection accuracy while keeping the false-alarm rate low. ![]() High-level block diagram of USPD algorithm. The signal is preprocessed and then passed to a neural-network-based classifier to detect motion. This signal gets reflected from a moving object and is then captured by the onboard microphone array. On the signal transmitter side, a device- and environment-dependent optimal ultrasound signal is transmitted through the onboard loudspeaker. So we instead use deep learning, which should be able to recognize more heterogeneous patterns in the signal.īelow is a high-level block diagram of our USPD algorithm. These complications mean that conventional signal processing is insufficient to recognize human motion from Doppler-shifted signals. As can be seen, they are difficult to tell apart. Below are two spectrograms, one of a room with no motion other than a rotating floor fan and another with both a fan and human motion near a device. Spectrogram of the signal with both the rotating floor fan and human motion.įurther, moving objects such as fans and curtains introduce their own Doppler shifts, which have to be rejected since they do not necessarily indicate people’s presence. In practice, what we observe looks more like this: In addition, when a person moves through a closed space, not only do we observe multiple Doppler components due to various parts of the body moving in different directions with different speeds, but we also observe repetitions of those components due to reflections.īecause of all these complexities, the signal received at the source is not at all as clean as a single tone with a frequency shift. The magnitude of the Doppler-shifted signal depends on factors such as distance from target to source, the size and absorption coefficient of the target, the absorption coefficient of the room, and even the humidity and temperature in the room. ![]() We use Doppler sensing due to the robustness of its motion detection signal and because it generalizes well across the cases when Alexa is or is not playing audio simultaneously. In time-of-flight sensing, variations in the arrival time of the reflected signal are monitored to detect changes in the environment. This frequency shift is similar to the shift in sound frequencies you hear in a police car siren it is approaching you or moving away from you.ĭoppler sensing detects motion by looking for frequency shifts in the recorded spectrum of a transmitted signal, which are caused by reflection from moving objects. In Doppler sensing, once the signal is transmitted, the system detects motion by looking for frequency shifts in the recorded spectrum of the signal, which are caused by its reflection from moving objects. Ultrasound sensors can be broadly categorized as using Doppler sensing or time-of-flight sensing. With ultrasound-based presence detection (USPD), an ultrasonic signal (>=32 kHz) is transmitted via onboard loudspeakers, and changes in the signal received at the microphones are monitored to detect motion. Getting the technology to work on existing Echo hardware required innovation on a number of fronts - among other things, reducing false alarms by adequately sampling long-tail data devising a self-calibration feature to adjust to variations in commodity hardware and filtering out distortion during concurrent ultrasound detection and music playback. There are many different motion detection technologies, but we selected ultrasound because it works in low-light conditions or even in the dark and, unlike radio waves, ultrasound waves do not travel through drywall, so there's less risk of detecting motion in other rooms. For instance, Routines could be configured to automatically turn the lights on, play music, or announce weather or traffic when motion is detected near a customer’s Echo device, indicating that someone has entered the room. Last fall, Amazon introduced ultrasound-based motion detection, to enable Alexa customers to initiate Routines, or prespecified sequences of actions, when certain types of motion are detected (or not detected). An example of how to use the Alexa app to configure Routines with ultrasound-based motion-detection triggers.
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