Systems and methods for data compression which facilitate the diagnosis and treatment of neurodevelopmental and neurodegenerative disorders. The methods comprise performing the following operations by a computing device: generating Normalized Data (“ND”) from Original Data (“OD”) that defines a Normalized Waveform (“NW”) that is unitless and scaled from zero to one; processing ND to extract Micro-Movement Data (“MMD”) defining a Micro-Movement Waveform (“MMW”) comprising a plurality of MMD points; and generating compressed data comprising a stochastic signature of MMW. Each MMD point determined based on a value of a peak of NW and a value representing an average of all data point values between a first valley of NW immediately preceding the peak and a second valley of NW immediately following the peak. The stochastic signature is defined by empirically estimated values of at least one parameter representing a Probability Distribution Function (“PDF”) of a continuous family of PDFs.

The movement of a part of human body, e.g. a limb, is monitored. The movement includes N iterated rotations in a plane. A sensing device is fixed to the limb and provides data indicative of the limb movement. These data are processed to generate a rotation signal. Then, portions of the rotation signal corresponding to each movement iteration are identified. To this purpose, a plurality of null-velocity instants is identified, wherein the angular velocity of the sensing device is null. Then, each null-velocity instant is classified as a candidate start/end time or a candidate peak time. Then, a start time, end time and peak time for each iteration are determined as a combination of two candidate start/end times and a candidate peak time that fulfils certain conditions on the values of the rotation signal in correspondence of candidate start/end times and candidate peak times.

The invention relates to a method of testing an infant, comprising the following steps: displaying a target on a screen; detecting contact made by the infant with the screen inside and/or outside the target; calculating a success parameter on the basis of the contact detected; and recording the calculated success parameter in a memory.

A movement-assessment device and methods for using the testing device includes a peg board device having a plurality of apertures on a top surface, and a plurality of photo-optical gate sensors. A computing device, comprising a touchscreen interface, communicates with the peg board device.

Disclosed is a rehabilitation training apparatus including a pair of first tracks that are arranged in parallel at an interval, a second track that is perpendicular to the pair of first tracks and is movably connected to the pair of first tracks, a hand holder that is movably provided in the second track and on which a hand of the user is held, a holder driving unit that reciprocally moves the hand holder along the second track, and a track driving unit that reciprocally moves the second track along the pair of first tracks.

A lower limb spasticity measurement method includes the step of setting the lower limbs of the person in a lower limb orthotic device of a gait activity machine, the step of starting up a motor of the gait activity machine to drive the lower limb orthotic device for lower limb activity, the step of getting a statistical distribution data from the output torque of the motor within a predetermined time and then calculating the statistical distribution data to obtain a threshold, and the step of determining whether the output torque of the motor is greater than the threshold or not, and then stopping motor if the output torque of the motor is greater than the threshold. Thus, the method of the invention can accurately measures spasticity in the lower limbs of a person without the use of sensors, effectively saving the cost of equipment.

A method of calculating in vivo force on an anterior cruciate ligament (ACL) by measuring one or more biomechanical properties during a biomechanical screening task to obtain one or more biomechanical datum from the measured one or more biomechanical properties, and calculating a total load on an anterior cruciate ligament from an ACL force model using the one or more biomechanical datum as inputs to the ACL force model. The ACL force model is defined by F.sub.ACL=F.sub.ACL.sup.sag+F.sub.ACL.sup.front+F.sub.ACL.sup.trans+Σ.sub.jCT.sub.j, wherein F.sub.ACL is the total force on the ACL, F.sub.ACL.sup.sag is the force on the ACL in a sagittal plane, F.sub.ACL.sup.front is the force on the ACL in the frontal plane, F.sub.ACL.sup.trans is the force on the ACL in the transverse plane, and CT.sub.j is the ACL force interaction relationships among the sagittal-frontal (SF), sagittal-transverse (ST), and frontal-transverse (FT) planes, where j=SF, ST, FT.

The present invention relates to a device for assessing the ability of a person to carry out one or more activities, comprising an input unit for receiving physiological and/or behavioural data of the person, the physiological and/or behavioural data being related to one or more first level activities, a determination unit for determining a performance grade of the person regarding each first level activity based on the received physiological and/or behavioural data, and an assessment unit configured to assess an ability grade of the person to carry out one or more second level activities based on the determined performance grade, wherein each second level activity relates to one or more associated first level activities, wherein the assessment unit is further configured to output information indicative of the assessed ability grade.

A system for remote motor and sensory function testing may involve use of patent and clinician computing devices, a patient testing device, and a clinician control device. A Remote Testing System (RTS) may coordinate displays of information among the devices in support of a test. A clinician or the RTS may cause display of instructions for guiding a patient in using the patient testing device. A clinician may receive feedback from the patient's interaction during the test session, or during a later session. The clinician control device may be used to control functioning of the patient testing device, in support of the test. The clinician control device may provide haptic or other feedback during the test session or during a later session. The system may track patient performance across multiple sessions and initiate follow-up test sessions as a result of data gathered in one or more prior test sessions.

An automated method for cognitive training of the brain of a human using contralateral movement is provided wherein the body of the human is defined, in part, by having contralateral sides and the human body has a plurality of motion tracking sensors attached to respective limbs of the human body.