Sleep apnea is a very common, yet severely under-diagnosed disorder characterized by reoccurring periods of shallow or paused breathing during sleep. If a breathing disruption causes the oxygen saturation in the blood to become too low, the brain will force an awakening to resume normal breathing. These awakenings are often very brief, making it unlikely for the sufferer to remember continuously waking up at night. The gold standard for diagnosing sleep apnea is polysomnography, which is a sleep study requiring the subject to spend the night in a laboratory with many physiological sensors attached to the body. This process is very resource demanding, and also very tedious and uncomfortable for the patients. Instead of providing alternatives to traditional polysomnography, our objective is to allow people to perform the first step towards a sleep apnea diagnosis at home. The core idea is to drastically reduce the cost and number of required sensors by utilizing smartphones, low-cost consumer grade sensors, and data mining techniques. Nevertheless, the realization of this idea assumes that the low-cost consumer grade sensors produce signals of adequate quality. In this thesis, we evaluate the signal quality of four respiratory effort sensors: a piezoelectric effort belt (PZT) from BITalino, an impedance plethysmography (IP) sensor from Shimmer, a respiratory inductance plethysmography (RIP) sensor (RespiBAN) from biosignalsplux, and a strain-gauge sensor (FLOW) from SweetZpot. We use a RIP sensor from NOX Medical as the gold standard. Instead of recreating the setting of traditional polysomnography, we design a sixteen-minute signal capture procedure to simulate epochs of disrupted breathing, which can be performed during wakefulness. With this procedure, we capture data from a total of twelve (BITalino and Shimmer) and eleven (RespiBAN and FLOW) external subjects, resulting in a total of 212 different signals for quality evaluation. Our signal quality evaluation approach is based on the breath detection accuracy metrics sensitivity, positive predictive value (PPV), and clean minute proportion (CMP), along with the breath amplitude accuracy metric weighted absolute percentage error (WAPE). These metrics are closely related to how apneic and hypopneic episodes are scored by medical personnel, making it trivial 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. Respectively, 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%. Their CMP is 60.93%, 71.72%, 49.50%, and 73.08%. 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, and while both BITalino and Shimmer show a correlation between signal quality and body mass index (BMI), the supine position is less affected overall compared to the side position.