This example shows how to check for the presence of a mobile phone. The code was based on the article 'Implementing Bluetooth Proximity Detection with Asterisk, Part II' http://snipr.com/1vvi5 [nerdvittles.com]

#!/usr/bin/ruby
#file: whereib.rb

deviceid = '00:0E:6D:29:38:EB'
devicename = 'Nokia 6600'

count = 0
while count < 1
  if `hcitool name #{deviceid}`.chomp == devicename 
    puts devicename + ' IN RANGE'
    puts Time.now
  else
    puts devicename + ' OUT OF RANGE'
    puts Time.now
  end
  sleep 7
end  

It's an unfortunate necessity that you need to write different code for windows sometimes or at the very least load different libraries since there is no fork(), etc. This is how you can tell.

# Returns true if we are running on a MS windows platform, false otherwise.

def Kernel.is_windows?
  processor, platform, *rest = RUBY_PLATFORM.split("-")
  platform == 'mswin32'
end

Inspired from the PIL version here
http://gumuz.looze.net/wordpress/index.php/archives/2005/06/06/python-webcam-fun-motion-detection/

First with typical import and Canvas, exit setup

from appuifw import *
from graphics import Image
import camera, e32
#import miso    # don't dim the light

app.body = c = Canvas()

running = 1
def quit():
    global running
    running = 0
app.exit_key_handler=quit

Then the getdata function that reads pixel data
from image by saving/reading PNG.

def getdata(im, bpp=24):
    import struct, zlib
    im.save('D:\\pixels.png', bpp=bpp, compression='no')
    f = open('D:\\pixels.png', 'rb')
    f.seek(8 +8+13+4)
    chunk = []
    while 1:
        n = struct.unpack('>L', f.read(4))[0]
        if n==0: break  # 'IEND' chunk
        f.read(4) # 'IDAT'
        chunk.append(f.read(n))
        f.read(4)   # CRC
    f.close()
    return zlib.decompress(''.join(chunk))  # '\x00' prefix each line

Lastly, the real code follows.

last1 = '\x00' * 930    # can be anything
while running:
    im = camera.take_photo('RGB', (160,120))
    im.rectangle([(10,10),(40,40)], 0xff0000)   # red outline
    im.rectangle([(120,10),(150,40)], 0xff0000) # no code for this square
    # check hot spot whether active
    box = Image.new((30,30), 'L')  # gray scale
    box.blit(im, (10,10,40,40))
    data = getdata(box, 8)
    # check difference for motion
    pixdiff = 0
    for i in range(len(data)):
        if abs(ord(data[i])-ord(last1[i])) > 15:  # pix threshold 15/256
            pixdiff += 1
            if pixdiff > 90:    # img threshold 90/900
                im.rectangle([(10,10),(40,40)], fill=0xff0000)  # fill
                break           # motion detected
    last1 = data
    c.blit(im, (0,0), (8,12))   # show camera
    #miso.reset_inactivity_time()

When measured, it takes around 1.1 sec for each loop.
Compared this with 0.9 sec without image processing,
out code is quite efficient.