1. A computer-implemented method for estimating falls risk, comprising: measuring first acceleration data from a first inertial sensor and second acceleration data from a second inertial sensor, the first and second inertial sensors comprise tri-axial accelerometers and are attached to at least one person transitioning at least one times from a standing state to a sitting state or from a sitting state to a standing state, wherein the first inertial sensor is attached to the person's lower body and the second inertial sensor is attached to the person's upper body; receiving, at one or more processors, the first acceleration data from the first inertial sensor attached to the at least one person; receiving, at the one or more processors, the second acceleration data from the second inertial sensor attached to the at least one person; receiving, at the one or more processors, falls history information of the at least one person; determining, from the first acceleration data and using the one or more processors, a first value of one or more features indicating a total time for the at least one person to complete the transitioning between the sitting state and the standing state; determining, from the second acceleration data and using the one or more processors, a second value of the one or more features indicating steadiness of movement of the at least one person, wherein a jerk of the at least one person's movement is calculated as a derivative of second acceleration data along a sensor axis to measure the steadiness of movement of the least one person along the sensor axis; determining, from the second acceleration data and using the one or more processors, a third value of the one or more features indicating a mean, coefficient of variation, or root mean square of the second acceleration data and a fourth value of the one or more features indicating a spectral edge frequency of the second acceleration data; generating, at the one or more processors, a classifier model based on the determined first value, the determined second value, the determined third value, the determined fourth value, and the falls history information of the at least one person; after the generation of the classifier model, the one or more processors receiving acceleration data from inertial sensors attached to another person; inputting a subset of the one or more features as input features of the classifier model; calculating, via the one or more processors using both the generated classifier model and the received acceleration data from the another person, a quantitative value for a probability of a risk of falling of the another person; and outputting a classification of likely to fall or not being likely to fall for the another person based on the calculated probability using the one or more processors.