Obstructive Sleep Apnea (OSA) is a common, but severely under- diagnosed sleep disorder characterized by recurring periods of shallow or paused breathing during sleep. It is our long-term goal to allow people to perform the first step towards a sleep apnea de- tection at home by utilizing smartphones, low-cost consumer-grade sensors, and data mining techniques. In this work, we evaluate the signal quality of four respiratory effort sensors (BITalino, FLOW, RespiBAN, and Shimmer), using a RIP sensor from NOX Medical as the gold standard. We design a sixteen-minute signal capture procedure to simulate epochs of disrupted breathing, and capture data from twelve (BITalino and Shimmer) and eleven (RespiBAN and FLOW) subjects during wakefulness. Our signal quality eval- uation approach is based on the breath detection accuracy met- rics sensitivity and positive predictive value (PPV), along with the breath amplitude accuracy metric weighted absolute percentage er- ror (WAPE). These metrics are closely related to how apneic and hypopneic episodes are scored by medical personnel, making it straightforward to reason about their interpretation. Our results show that false breaths are the primary concern affecting the breath detection accuracy of BITalino, Shimmer, and RespiBAN. Respec- tively, the sensitivity of BITalino, Shimmer, RespiBAN, and FLOW is 99.61%, 98.53%, 98.41%, and 98.91%. Their PPV is 96.28%, 96.58%, 90.81%, and 98.81%. Finally, their WAPE is 13.82%, 16.89%, 13.60%, and 8.75%. The supine (back) position is consistently showing the overall best signal quality compared to the side position.