In this paper we present a low-intrusive approach to the detection of expressive full-body movement qualities. We focus on two qualities: Lightness and Fragility and we detect them using the data captured by four wearable devices, two Inertial Movement Units (IMU) and two electromyographs (EMG), placed on the forearms. The work we present in the paper stems from a strict collaboration with expressive movement experts (e.g., contemporary dance choreographers) for defining a vocabulary of basic movement qualities. We recorded 13 dancers performing movements expressing the qualities under investigation. The recordings were next segmented and the perceived level of each quality for each segment was ranked by 5 experts using a 5-points Likert scale. We obtained a dataset of 150 segments of movement expressing Fragility and/or Lightness. In the second part of the paper, we define a set of features on IMU and EMG data and we extract them on the recorded corpus. We finally applied a set of supervised machine learning techniques to classify the segments. The best results for the whole dataset were obtained with a Naive Bayes classifier for Lightness (F-score 0.77), and with a Support Vector Machine classifier for Fragility (F-score 0.77). Our approach can be used in ecological contexts e.g., during artistic performances.