The learning apparatus includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

Presented are concepts for monitoring walker usage by a subject. One such concept obtains movement data comprising values of detected movement of an arm of the subject for a first time period during which the subject is determined to have transferred between first and second locations the first time period. It is determined if the subject used a walker during the first time period based on a measure of variance in the values of detected movement of the subject's arm during the first time period.

Embodiments disclosed herein are directed to personal therapy and exercise systems as well as to methods related thereto. For example, a personal therapy system can be a modular system that can include multiple therapy gear modules.

An compression therapy device which improves on available products by combining the efficacy of bulky, active pneumatic devices with a convenience approaching that of a passive non-pneumatic device. This is achieved by using an air cell (6) with a flexible inner and a rigid outer wall (10) which enables therapeutic pressures to be achieved at lower overall inflation volume. This in turn allows miniature low power, silent pump (3) to be used enabling the device to be worn unobtrusively under clothing. Such a device is targeted at users with long term chronic venous conditions, variants of oedema, recovering from surgery, requiring travel comfort, improved leg circulation or accelerated recovery from exercise or injury.

An assistive device structure for positioning and pressure relief is provided, including a first elastic layer and a second elastic layer, which are attached by using a high-frequency encapsulation process, sealing, bagging, thermoforming, or an integrally molding process. Each of the first and second elastic layers has a bottom surface and an arc surface disposed opposite to each other. The arc surface includes two protrusions and a recess formed there in between. The two protrusions have different heights. A hollow area is disposed in the recess of the first and second elastic layers. Based on such structure, the bottom surfaces of the first and second elastic layers are attached to form the proposed assistive device structure for a user to lean against and providing multiple positioning effects and pressure relief. More than four axial directions of supporting forces are generated to effectively enhance muscle relaxation and stress relief.

A robotic system, assembly and computer assisted media for providing therapeutic treatment not limited to muscle lengthening. An adjustable probe is mounted to a carriage, in turn supported in width and depth extending relationship upon a portable trolley supported frame or other movable carriage which can be positioned over a patient support device for applying treatment to a given patient muscle areas according input parameters selected from any of heat, cold, vibration (frequency), pulse pressure and duration. A processor input for any of a PC, tablet, laptop or smartphone with mobile application communicates with the software component for providing directions to actuate the probe to apply a treatment protocol. The input can be communicated remotely via NFC, Bluetooth or Cloud capabilities with a remote care provider or ACO organization.

A hippotherapy device includes a saddle for receiving a person thereon, a programmable movement control, and an automatic movement device for automatically moving the saddle according to a movement pattern program. The program specifies a sequence of target values of positions and orientations of the saddle in the locality of the movement device in order to execute the movement pattern. The hippotherapy device further includes a person securing device having a main support on which a body strap is mounted, at least one sensor for detecting at least one physical variable characterizing the body posture of the person on the saddle, and an auxiliary control device configured to compare expected values of the physical variable with current values of the physical variable detected by the sensor, and to trigger an associated safety function, if a deviation of the current values from the expected values exceeds a predetermined tolerance threshold.

The learning system includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. A sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. The data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. The learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.

A system for obtaining and analyzing data for overall joint motion from a plurality of joints of a subject experiencing a movement disorder involves a plurality of kinematic sensors configured to be placed on a body of a subject experiencing a movement disorder proximal a plurality of joints of the subject. The kinematic sensors are selected to measure overall joint motion with sufficient degrees of freedom for individual joints so that data collected by the sensors can be deconstructed into multiple degrees of freedom for individual joints and analyzed to provide amplitude of the movements caused by the movement disorder and/or relative contributions from and/or directional bias for each muscle group that may be implicated in the movement of each joint. Such a system permits methods for determining a treatment regimen for treating the movement disorder, whereby the treatment regimen is based on the amplitude of the movements and/or the relative contribution and/or directional bias of each muscle group to the movements caused by the movement disorder.

Apparatus and associated methods relate to a walking therapy station having multiple right linkages and multiple left linkages, where at least one of the right or left linkages is operably coupled to an actuator to transition the station from a standing mode to a walking mode. In an illustrative example, the station may have five right linkages and five left linkages, with a set of knee pads and foot pads. The station may include an actuator operably coupled to transition the station between walking and standing modes, for example. Various embodiments of the station may enable a user who is disabled or paralyzed to transition from sitting position, to a standing position, and then to a walking position, and provide the user with a very accurate gait and walking motion without putting excessive shear and pressure at the contact points between the station and the user.