An address-event fall detector for assisted living applications.

In this paper, we describe an address-event vision system designed to detect accidental falls in elderly home care applications. The system raises an alarm when a fall hazard is detected. We use an asynchronous temporal contrast vision sensor which features sub-millisecond temporal resolution. The sensor reports a fall at ten times higher temporal resolution than a frame-based camera and shows 84% higher bandwidth efficiency as it transmits fall events. A lightweight algorithm computes an instantaneous motion vector and reports fall events. We are able to distinguish fall events from normal human behavior, such as walking, crouching down, and sitting down. Our system is robust to the monitored person's spatial position in a room and presence of pets.

Orientation-selective aVLSI spiking neurons.

We describe a programmable multi-chip VLSI neuronal system that can be used for exploring spike-based information processing models. The system consists of a silicon retina, a PIC microcontroller, and a transceiver chip whose integrate-and-fire neurons are connected in a soft winner-take-all architecture. The circuit on this multi-neuron chip approximates a cortical microcircuit. The neurons can be configured for different computational properties by the virtual connections of a selected set of pixels on the silicon retina. The virtual wiring between the different chips is effected by an event-driven communication protocol that uses asynchronous digital pulses, similar to spikes in a neuronal system. We used the multi-chip spike-based system to synthesize orientation-tuned neurons using both a feedforward model and a feedback model. The performance of our analog hardware spiking model matched the experimental observations and digital simulations of continuous-valued neurons. The multi-chip VLSI system has advantages over computer neuronal models in that it is real-time, and the computational time does not scale with the size of the neuronal network.

Silicon retina with correlation-based, velocity-tuned pixels.

A functional two-dimensional silicon retina that computes a complete set of local direction-selective outputs is reported. The chip motion computation uses unidirectional delay lines as tuned filters for moving edges. Photoreceptors detect local changes in image intensity, and the outputs from these photoreceptors are coupled into the delay line, where they propagate with a particular speed in one direction. If the velocity of the moving edges matches that of the delay line, then the signal on the delay line is reinforced. The output of each pixel is the power in the delay line signal, computed within each pixel. This power computation provides the essential nonlinearity for velocity selectivity. The delay line architecture differs from the usual pairwise correlation models in that motion information is aggregated over an extended spatiotemporal range. As a result, the detectors are sensitive to motion over a wide range of spatial frequencies. The design of functional one- and two-dimensional silicon retinas with direction-selective, velocity-tuned pixels is described. It is shown that pixels with three hexagonal directions of motion selectivity are approximately (225 mum)(2) in area in a 2-mum CMOS technology and consume less than 5 muW of power.