A gait assistance slab is provided. The gait assistance slab includes a body that includes an outer surface. The gait assistance slab further includes a plurality of sensors disposed inside the body. The gait assistance slab further includes a plurality of actuators disposed inside the body. The gait assistance slab further includes circuitry communicatively coupled to the plurality of sensors and the plurality of actuators. The circuitry detects a presence of a person on the outer surface based on an electric signal acquired from one of the plurality of sensors. The circuitry determines a level of actuation of one of the plurality of actuators based on the detected presence of the person and the acquired electric signal. The circuitry further controls, based on the determined level of actuation, the one of the plurality of actuators to assist gait of the person on the outer surface.

Systems and methods are disclosed to determine a measure of patient weight using existing medical device sensors, comprising receiving acceleration information of a patient and, if a value of the acceleration information exceeds an activity threshold over a measurement window, detecting patient steps in the measurement window using the acceleration information, determining a patient step rate over the measurement window using the detected patient steps, determining a measure of patient step force for the measurement window, and determining a measure of patient weight using the determined patient step rate and measure of patient step force.

Apparatus for providing transcutaneous electrical nerve stimulation (TENS) therapy to a user, said apparatus comprising: a stimulation unit for electrically stimulating at least one nerve of the user; a sensing unit for sensing body movement of the user to analyze body movement activity type and activity duration; an application unit for providing mechanical coupling between said sensing unit and the user's body; and a feedback unit for at least one of (i) providing the user with feedback in response to said analysis of said body movement activity type and activity duration of the user, and (ii) modifying the electrical stimulation provided to the user by said stimulation unit in response to said analysis of said body movement activity type and activity duration of the user.

In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

In one embodiment, a method for generating a message to a friend of a user is provided, comprising: processing activity data of a first user measured by an activity monitoring device to update a value of an activity metric for the first user; identifying a change in an inequality relationship between the value of the activity metric for the first user and a value of the activity metric for a second user; in response to identifying the change in the inequality relationship, prompting the first user to generate a message to the second user.

Aspects relate to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.

A data processing system comprising a processing device configured to collect sets of sensed data representing motion characteristics of a user's gait at a first time and at a second time subsequent to the first time, to process each set of sensed data to form a respective information set dependent on the user's gait at the first and second times, and to compare the first information set with one of (i) the second information set and (ii) a predetermined reference data set in accordance with a predetermined algorithm to identify a condition or change in condition of the user.

A method of determining a CRF level for a user of a fitness tracking system includes receiving activity data from at least one activity sensor carried by the user during a number of workouts, the activity data including distance data for each of the number of workouts, and then generating workout data based on the activity data. The method further includes storing the workout data in a memory, the memory further including demographic data for the user. When the an attribute of the workout data is less than a threshold number, the method includes determining a first CRF level for the user based on a first CRF model. When the attribute of the workout data is greater than the threshold number, the method includes determining a second CRF level for the user based on a second CRF model, and displaying the first and second CRF levels.

Provided is a display control method for a display control device including a processor and a storage. The method includes generating first display data for a display to display user marks at intervals so as to be added one by one as a time elapses from a timing. The intervals each correspond to a user's step length of a user obtained based on measured data on running or walking of the user. The user marks each represent a position where a foot of the user lands by one step. The method further includes generating second display data for the display to display reference marks at intervals so as to be added one by one as the time elapses. The intervals each correspond to a predetermined reference step length. The reference marks each represent a position where a reference foot lands by one step.

A system and a method for predicting kinesthetic outcomes from observed position, posture, behavior or activity of an individual 1602, 1702. The system uses kinesthetic activity sensors 102, 104 each collecting one or more of audio, video, or physiological signals and capturing the activity of the individual or an ambient environment of the individual. These signals are delivered into a computer system 106 implementing a learning routine 108 which constructs one or more personalized kinetic state models 1510 of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models 1500 for the individual and uses the multidimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future, and provides notice of predicted unsafe or undesired outcomes.