Over the past decades, valvometric techniques have been commonly used to record valve opening activities of bivalves. Various relationships with environmental variations have been elucidated through different types of metrics extracted from valvometric signals (e.g. valve opening, cyclicity, specific behaviours). Although automated data processing methods exist, many specific behaviours are still annotated manually. This study proposes an algorithm to detect and classify the behaviours performed by the great scallop (Pecten maximus) in two categories: jump-like (JL) behaviours and other behaviours (OBs). These two categories differ in the shape of their valvometric signal, JL being movements of high amplitudes associated with ‘displacement movements’ (rotation, swimming, jumping, flipping) and OB grouping all other movements of lower amplitudes (‘common movements’), such as partial closures, which are produced routinely. This algorithm has been developed and tested on 10 scallop valve opening time series recorded using fully autonomous valvometers based on the Hall effect principle. The algorithm detected 93.65% ± 5.5 of manually annotated behaviours produced by scallops, with a false detection rate of less than 6.3% ± 5.5. Classification performances vary according to the type of behaviour. JL behaviours and OBs were well classified at 83.72% ± 23.09 and 98.92% ± 1.80, respectively. Analysis of the algorithm's outputs, highlighting potential daily trends in the production of certain behaviours, shows their relevance for acquiring information on the biology of scallops. By providing an efficient and flexible detection and classification method, this study is a first step towards the automation of bivalve behaviour detection. This study also highlights the importance of simultaneously using Hall sensors and accelerometers to accurately classify the complex behaviours of mobile bivalves such as P. maximus.