With an increasing number of robotic and prosthetic devices, there is a need for intuitive interfaces which enable the user to efficiently interact with them. The conventional interfaces are generally bulky and unsuitable for dynamic and unstructured environments. An alternative to the traditional interfaces is the class of Muscle-Machine Interfaces (MuMIs) that allow the user to have an embodied interaction with the devices they are controlling. In this work, we present a wearable, lightweight, Forcemyography (FMG) based armband for Human-Machine Interaction fabricated entirely out of 3D-printed parts and silicone components. The armband uses six force sensing units, each housing an Force Sensitive Resistor (FSR) sensor. The capabilities of the armband are evaluated while decoding four different gestures (pinch, power, tripod, extension) and rest state and its performance is compared with a state-of-the-art Electromyography (EMG) bioamplifier. The decoding performance of the decoding models trained on the data acquired from the armband is significantly better than the performance of the models trained on raw EMG data. The hardware design and the related processing software, are disseminated in an open-source manner.