Identifying the finger used for touching and measuring the force of the touch provides valuable information on manual interactions. This information can be inferred from electromyography (EMG) of the forearm, measuring the activation of the muscles controlling the hand and fingers. We present Touch-Sense, which classifies the finger touches using a novel neural network architecture and estimates their force on a smartphone in real time based on data recorded from the sensors of an inexpensive and wireless EMG armband. Using data collected from 18 participants with force ground truth, we evaluate our system's performance and limitations. Our system could allow for new interaction paradigms with appliances and objects, which we exemplarily showcase in four applications.