Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, one or more alignment plans for aligning a user with an imaging sensor. The method comprises generating one or more machine learning models trained to identify alignment plans. The method also comprises receiving user data and determining that a targeted portion of a body of the user is outside of a field of view of the imaging sensor. The method further comprises generating the one or more alignment plans using the one or more machine learning models. Each of the one or more alignment plans comprises a target location within the field of view of the imaging sensor and one or more elements for adjusting the targeted portion of the body from a first location to the target location. The method also comprises transmitting the one or more alignment plans to a computing device.

A locking device including a first frame including an insert hole and a first magnetic body, a second frame including an insert recess communicating with the insert hole, and a second magnetic body disposed to face the first magnetic body with a polarity opposite to a polarity of the first magnetic body, and an insert member including a slider inserted into the insert hole and configured to slide relative to the first frame, and a third magnetic body disposed on one side of the slider is provided.

A platform supports a person in a supine position. A pelvic restraint secures the person's hips to the platform. A left leg armature is rotatably connected to the platform, with an axis of rotation of the left leg armature positioned at a prescribed distance from an axis of the person's left hip joint when the hips are secured to the platform. The left leg armature lifts the left leg while pushing the left hip into the platform, and maintains extension of the left knee. A right leg armature is rotatably connected to the platform with an axis of rotation of the right leg armature positioned at a prescribed distance from an axis of the person's right hip joint when the hips are secured to the platform. The right leg armature lifts the right leg while pushing the right hip into the platform, and maintains extension of the right knee.

A computer implemented method of semi-supervised intent recognition for an exoskeleton system. In one aspect, the method includes, in response to a state transition intention input, changing the exoskeleton system from operating in a first mode with sensitivity to detecting state transitions at a first sensitivity level to operating in a second mode with sensitivity to detecting state transitions at a second sensitivity level that is more sensitive than the first sensitivity level; identifying a state transition while operating in the second mode and using the second sensitivity level; and facilitating the identified state transition by actuating the exoskeleton system.

A device and method are provided for treating a patient with vibration and/or tactile and/or thermal stimuli. The device includes first and second stimulating units for generating first and second stimuli respectively. The stimuli are vibration and/or tactile and/or thermal stimuli, and each of the stimuli is repeated on average at a frequency of 1 to 60 Hz. The stimuli are generated partly at different times.

According to some embodiments, a device configured for self-administered therapy comprises at least two base members, each base member configured to be secured to a surface for mounting the device of said surface, a shaft configured to secure to the at least two base members and extend between the at least two base members, and at least one therapy member configured to be secured relative to the shaft, wherein the at least one therapy member is configured to rotate relative to the shaft during use, wherein the at least one therapy member is configured to be contacted by a user and configured to rotate during use.

A computer-implemented method for (1) receiving one or more characteristics of a user, wherein the one or more characteristics comprise personal information, performance information, measurement information, or some combination thereof, (2) generating, using one or more trained machine learning models, a treatment plan for the user, wherein the treatment plan is generated based on the one or more characteristics of the user, and the treatment plan comprises: (i) a dietary plan that is tailored to manage one or more medical conditions associated with the user, and (ii) an exercise plan comprises one or more exercises associated with the one or more medical conditions, and (3) presenting, via a display device, at least a portion of the treatment plan comprising the dietary plan.

The current invention relates to a compression device wearable by a user for applying compression on a left lower limb and a right lower limb of the user. The compression device includes a left massager sleeve, a right massager sleeve, a computing device, a left air pressure hose, and a right air pressure hose. The left massager sleeve and the right massager sleeve are adapted to surround and secure the left lower limb, and the right lower limb respectively. The computing device generates air pressure. The computing device includes a control panel to control the flow of the air pressure in the left massager sleeve and the right massager sleeve. The computing device includes a battery to provide power supply to the control panel. The left air pressure hose supplies air pressure to the left massager sleeve. The right air pressure hose supplies air pressure to the right massager sleeve.

A soft tissue mobilization device having a hand-held portion coated with a compressible material to take the strain off the hand of the user when using the device. The hand-held portion is coated with a compressible material to take a strain off the hand of the user, is custom molded to the user's hand, and the compressible material is a compound comprising a mixture of one or more low melting point polymers.