With the increasing popularity, diversity, and utilization of wearable devices, the data transfer-and-storage efficiency becomes increasingly important. This paper evaluates a set of compression techniques regarding their utilization in crowdsourced wearable data. Transform-based Discrete Cosine Transform (DCT), interpolation-based Lightweight Temporal Compression (LTC) and dimensionality reduction-focused Symbolic Aggregate Approximation (SAX) were chosen as traditional methods. Additionally, an altered SAX (ASAX) is proposed by the authors and implemented to overcome some of the shortcomings of the traditional methods. As one of the most commonly measured entities in wearable devices, heart rate data were chosen to compare the performance and complexity of the selected compression methods. Main results suggest that best compression results are obtained with LTC, which is also the most complex of the studied methods. The best performance-complexity trade-off is achieved with SAX. Our proposed ASAX has the best dynamic properties among the evaluated methods.