Understanding how animals allocate their time and resources across the diel cycle and in response to environmental conditions is fundamental to ecology and conservation. While the use of accelerometers has enabled detailed descriptions of activity patterns and energetics of animals, its application to small- to medium-sized animals remains limited. This study describes a methodological approach for monitoring activity budgets in those instances using purpose-built accelerometry and hidden Markov models (HMMs). We demonstrate this approach by investigating the activity profile of five Critically Endangered big-headed turtles, Platysternon megacephalum (2 females and 3 males). Using a user- and budget-friendly tri-axial accelerometer designed for small- to medium-sized animals, we collected high-resolution data on the locomotory behaviors of P. megacephalum. We categorized the accelerometry data into two distinct behavioral states: (i) stationary and (ii) mobile using HMMs in an unsupervised setting. Our approach successfully quantified key locomotory-related behaviors in a cryptic species, revealing that individuals spend approximately 87% of their time stationary, with significant variations in activity related to sex, diel cycles, and weather conditions. Female turtles exhibited a crepuscular activity pattern, peaking at dawn (03:00–10:00) and dusk (17:00–20:00), while males demonstrated a more cathemeral profile (05:00–12:00). These results suggest that females and males could be unevenly targeted by poaching, with males being most susceptible to trap deployment. Notably, increased temperatures and precipitation positively correlated with increased activity levels within daily budgets, highlighting potential vulnerabilities to extreme weather events. This research presents an accessible pipeline that bridges biologging innovation and behavioral ecology, enabling targeted conservation strategies for small- to medium-sized animals by translating raw sensor data into actionable ecological insights. This is exemplified in our study of P. megacephalum, where we underscore the importance of understanding intrinsic behavioral traits and their interactions with extrinsic threats in P. megacephalum, providing critical conservation insights aimed at mitigating their population decline.