In this paper we describe a novel method for sensor placement identification, and demonstrate the effectiveness of this method on an upper limb neuroprothesis for tremor suppression under a variety of tasks. Our objective is to facilitate long-term tremor monitoring; tremor is the most prevalent movement disorder. Two assumptions are made: 1) movement and tremor demonstrate an additive effect further down the kinematic chain; 2) most applications have chained or fixed sensor locations. These assumptions justify obtaining absolute location through identifying relative location and thus to allow us to simplify the classification algorithm. Seventeen tasks were performed by patients suffering from essential tremor or Parkinson's disease. Ten features were found that resulted in 98.30% average accuracy (min: 92.31%; max: 100%) for the best configuration, irrespective of the task being performed. The method presented here is an important step towards more user-friendly and context-aware neuroprostheses for tremor suppression and monitoring, and facilitates the use of wearable sensors by non-trained personnel.