PV-Pix: Slum Community Co-design of Self-Powered Deformable Smart Messaging Materials

Raju, Dani Kalarikalayil; Seunarine, K.; Reitmaier, Thomas; Thomas, Gethin; Meena, Yogesh Kumar; Zhang, Chi; Pockett, Adam; Pearson, Jennifer; Robinson, Simon; Carnie, Matthew J.; Sahoo, Deepak Ranjan; Jones, Matt

doi:10.1145/3411764.3445661

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Recent work of light-sensing surfaces has opened a new design space that integrates sensing and computation into everyday objects we interact with, enabling a myriad of applications for gestural input to activity recognition without physical contact [9][10][11][12] . Flexible photodetectors based on emerging semiconductors 13 , such as organic photodiodes (OPDs), are desirable to develop light-sensing surfaces because they can be manufactured over large areas 14 , patterned into arbitrary shapes [15][16][17] , and with conformal form factors [18][19][20][21][22][23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

Flexible computational photodetectors for self-powered activity sensing

et al. 2022

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Conventional vision-based systems, such as cameras, have demonstrated their enormous versatility in sensing human activities and developing interactive environments. However, these systems have long been criticized for incurring privacy, power, and latency issues due to their underlying structure of pixel-wise analog signal acquisition, computation, and communication. In this research, we overcome these limitations by introducing in-sensor analog computation through the distribution of interconnected photodetectors in space, having a weighted responsivity, to create what we call a computational photodetector. Computational photodetectors can be used to extract mid-level vision features as a single continuous analog signal measured via a two-pin connection. We develop computational photodetectors using thin and flexible low-noise organic photodiode arrays coupled with a self-powered wireless system to demonstrate a set of designs that capture position, orientation, direction, speed, and identification information, in a range of applications from explicit interactions on everyday surfaces to implicit activity detection.

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