Abstract: A health monitoring method, system and computer program product comprising using a cellphone having an IMU, to (typically repeatedly) measure at least one gait parameter of a human bearing the cellphone during a first period in which the human is known to be dual-tasking; and/or at least one gait parameter of the human bearing the cellphone during a second period in which the human is known not to be dual-tasking; and/or using a hardware processor to receive the gait parameters and/or to compute at least one difference (in value e.g.) between gait parameter/s during the first period and (typically the same) gait parameter/s during the second periods, and, accordingly, to generate a cognitive health alert at least once e.g. according to an alert generation criterion defined over at least the difference/s so computed.

Abstract: A health management system comprising a personnel allocation processor for allocating personnel to conduct home or clinic sessions e.g. with diabetes patients who may be known to the system; and/or a gait analysis processor receiving IMU data e.g. from cellphones which may be worn by the diabetes patients and/or providing the personnel allocation processor with indications of a subset of the diabetes patients whose IMU data indicates new deterioration. Typically, allocation of personnel by the personnel allocation processor at least partly favors diabetes patients falling within the subset over diabetes patients who do not fall within the subset. The personnel allocation processor may comprise doctors' office software for managing appointments, which may have priority logic accepting prioritization e.g. of at least one patient in the subset over at least one patient not in the subset, via a machine interface.

Abstract: Method for processing users' cellphone data, the method comprising using a hardware processor for analyzing at least accelerometer (IMU) data provided by cellphones of at least a threshold number of users in a given location including recognizing at least one urban feature at said given location each time at least the threshold number of users, known to be present at the given location, exhibit a given motion pattern known by the processor to be characteristic of said urban feature; and/or generating a map including a stored representation of said at least one urban feature which is associated in memory with said given location at which the accelerometer data indicative of the urban feature was collected.

Abstract: A gait analysis method comprising providing a data flow generated by inertial sensor/s borne by an end-user who is ambulating, to define a data flow which describes the end-user's gait, and providing a hardware processor configured to derive, from the data flow which describes the end-user's gait, data which characterizes the end-user e.g. center of pressure (COP) trajectory data and/or kinematic data and, optionally, estimating validity of using the inertial sensor to estimate kinematic data and/or generating an output indication of the user's kinematics trajectory data only when the validity is over-threshold.

Abstract: A stroke detection system operative to detect strokes suffered by mobile communication device users, the system comprising: a hardware processor operative in conjunction with a mobile communication device having at least one built-in sensor; the hardware processor being configured to, typically repeatedly and typically without being activated by the device's user, compare data derived from the at least one sensor to at least one baseline value for at least one indicator of user well-being, stored in memory accessible to the processor, and/or make a stroke risk level evaluation; and/or perform at least one action if and only if the stroke risk level is over a threshold.

Abstract: A stroke detection system operative to detect strokes suffered by mobile communication device users, the system including a hardware processor operative in conjunction with a mobile communication device having at least one built-in sensor. The hardware processor is configured to, repeatedly and without being activated by the device's user, compare data derived from the at least one sensor to at least one baseline value for at least one indicator of user well-being, stored in memory accessible to the processor and/or make a stroke risk level evaluation; and/or perform at least one action if and only if the stroke risk level is over a threshold.

Abstract: A system capturing motion of a moving body, including an IMU interface to receive IMU measurements from IMU/s worn on the body; and a processor to derive, from the IMU measurements, a motion capture approximation output including a trajectory, which describes a body portion’s motion during a cycle of repetitive motion, yielding a trajectory set including B body portion trajectories, wherein, to derive the trajectory set, the processor uses generative adversarial networks including one network trained to determine physical feasibility of candidate body portion trajectory/ies for body portion/s, from among multiple candidate body portion trajectories for the specific body portion; and another network trained to determine how well candidate body portion trajectory/ies fit/s the IMU measurements.

Abstract: A stroke detection system operative to detect strokes suffered by mobile communication device users, the system comprising a hardware processor operative in conjunction with a mobile communication device having at least one built-in sensor; the hardware processor being configured to, repeatedly and without being activated by the device's user, compare data derived from said at least one sensor to at least one baseline value for at least one indicator of user well-being, stored in memory accessible to the processor and/or make a stroke risk level evaluation; and/or perform at least one action if and only if the stroke risk level is over a threshold.

Abstract: A gait monitoring system operative to monitor gait of an end-user bearing a wearable device equipped with at least one magneto-inertial sensor, the system comprising a processor configured to receive raw sensor data from the wearable device's at least one magneto-inertial sensor to extract situational data from the raw sensor data, the situational data including at least the device's bodily position relative to the end-user, to determine a gait analysis process which yields at least one parameter characterizing the end-user's gait, depending at least on the device's bodily position as extracted, and to compute, and generate an output indication of, the at least one parameter characterizing the end-user's gait, by running the gait analysis process as selected.