A possible answer is the approach of Hendershott, Jones, and Menkveld in their JF2011 paper (this paper was mentioned also in this post). From the introduction:
We use a normalized measure of NYSE electronic message traffic as a proxy for AT. This message traffic includes electronic order submissions, cancellations, and trade reports. Because we normalize by trading volume, variation in our AT measure is driven mainly by variation in limit order submissions and cancellations. This means that, for the most part, our measure is picking up variation in algorithmic liquidity supply.
They discuss their proxy for AT (Algorithmic Trading) in section II.A. The main intuition - as stated in the intro - is that the ratio between messages and executions has increased because of AT, through the various practices it allows (e.g.: fast repositioning of limit orders and cancellations).
[F]or each stock each month we calculate our AT proxy, algo tradit, as the number of electronic messages per $100 of trading volume.