A method for probabilistically determining an individual's sleep stage, the method comprising the following operations performed via one or more processors: receiving a set of signals from a set of sensors worn by the individual, the set of signals including a photoplethysmographic ("PPG") signal and an accelerometer signal; dividing the PPG signal into a set of equally-timed segments if the received PPG signal comprises segments having unequal time durations; determining a beat interval associated with each segment, the beat interval associated with each segment reflecting an elapsed time between successive heartbeats over the segment; sampling the set of beat intervals to generate an interval signal; generating a set of signal features based on the interval signal and the accelerometer signal, the set of signal features including a spectrogram of the interval signal wherein the spectrogram reflects the distribution of the interval signal transformed into the frequency domain; and determining a sleep stage for the individual by operating on the set of signal features using a sleep stage classifier included in a learning library, wherein the sleep stage classifier comprises a set of functions defining a likelihood that the individual is in the sleep stage based on the set of signal features, wherein the set of functions includes signal weights, and wherein each signal weight is based on a correlation between a feature in the set of signal features and a known sleep stage; wherein the method further comprises: sampling the accelerometer signal at a first sampling rate; differentiating the sampled accelerometer signal over each channel associated with the accelerometer signal; and averaging a magnitude of the differentiated accelerometer signals over an accelerometer time window to generate a time-averaged motion signal; wherein the set of signal features on which the sleep stage classifier operates further comprises the time-averaged motion signal such that the sleep stage is further based on the time-averaged motion signal. receiving a set of signals from a set of sensors worn by the individual, the set of signals including a photoplethysmographic ("PPG") signal and an accelerometer signal; dividing the PPG signal into a set of equally-timed segments if the received PPG signal comprises segments having unequal time durations; determining a beat interval associated with each segment, the beat interval associated with each segment reflecting an elapsed time between successive heartbeats over the segment; sampling the set of beat intervals to generate an interval signal; generating a set of signal features based on the interval signal and the accelerometer signal, the set of signal features including a spectrogram of the interval signal wherein the spectrogram reflects the distribution of the interval signal transformed into the frequency domain; and determining a sleep stage for the individual by operating on the set of signal features using a sleep stage classifier included in a learning library, wherein the sleep stage classifier comprises a set of functions defining a likelihood that the individual is in the sleep stage based on the set of signal features, wherein the set of functions includes signal weights, and wherein each signal weight is based on a correlation between a feature in the set of signal features and a known sleep stage; wherein the method further comprises: sampling the accelerometer signal at a first sampling rate; differentiating the sampled accelerometer signal over each channel associated with the accelerometer signal; and averaging a magnitude of the differentiated accelerometer signals over an accelerometer time window to generate a time-averaged motion signal; sampling the accelerometer signal at a first sampling rate; differentiating the sampled accelerometer signal over each channel associated with the accelerometer signal; and averaging a magnitude of the differentiated accelerometer signals over an accelerometer time window to generate a time-averaged motion signal; wherein the set of signal features on which the sleep stage classifier operates further comprises the time-averaged motion signal such that the sleep stage is further based on the time-averaged motion signal.