The purpose of the present invention is: to providing a manual muscle strength testing device with which it is possible, by using an inertial sensor and a load exercise measurement device of simple configuration, to capture very small muscle contractions of the fingers while suppressing the compensatory effects of surrounding muscles; and to make it possible to evaluate the state or the degree of recovery of the median, ulnar, and radial nerves by measuring only a load exercise of the index finger.

A manual muscle strength testing device according to the present invention has, inter alia: a finger insertion portion 30, comprising an other finger fixation portion 28 having a space that is shaped so as to prevent movement, in any direction, of a middle, a ring, or little finger inserted thereinto, and an index finger insertion portion 29 having a space that is shaped such that an inserted index finger is able to move towards the ventral side or the thumb side, or the nail side or the thumb side; a ring 15 that can be fitted onto the fingertip of the index finger; a pressure sensor 32 capable of measuring the magnitude of a load applied on the fingertip and a 3-axis acceleration sensor 11 capable of measuring the movement of the fingertip, fixed to the ring 15; and a load elastic body 31 that applies an adjustable load to the fingertip on which the ring 15 is worn.

The invention relates to a measuring system for reproducibly measuring reaction time curves in the case of a complex neurocognitive task. For this purpose, human influences are largely prevented when carrying out the experiment. As a result of external data processing, the measuring system is able to form an independently growing and anonymous data basis which increases in accuracy due to the continuously increasing amount of data therein. This also allows statements to be made about potentially dangerous changes in reaction times up to the indication and/or identification of neurodegenerative diseases.

The present disclosure relates to a computer-implemented method for monitoring hand movements of a writing instrument's user, comprising: providing an electromyography sensor on the user's wrist or hand; monitoring hand movements of the user during a writing session with the writing instrument by reading sensors of the writing instrument; monitoring hand muscle activity of the user during the writing session by reading the electromyography sensor; correlating hand motion data and hand muscle data obtained from the monitoring; evaluating the correlated data and classifying the hand movements as normal or abnormal based on at least one of tremor parameters, hypokinetic parameters, and historical data of the user; and providing an indication in case of an abnormal evaluation.

A forearm assessment and training device has a main support, a plurality of finger motion transmission members, a plurality of finger receivers, and a control module. Each of the finger motion transmission members has a member body with a first end and a second end. The first end of the member body of each of the finger motion transmission members is connected to the main support. Each of the finger receivers is connected to the member body of one of the finger motion transmission members. Each of the finger receivers has a finger aperture. The control module is connected to the main support. The control module includes a control module processor, a control module memory, and a sensor. The sensor is configured to measure a force applied to at least one of the finger motion transmission members.

A wearable electronic device is provided. The wearable electronic device includes a biosensor including at least one light emitter and at least one light receiver, the biosensor being configured to sense blood flow of a user wearing the wearable electronic device, communication circuitry, an output interface, memory, comprising one or more storage media, storing instructions, and one or more processors communicatively coupled to the biosensor, the communication circuitry, the output interface, and the memory, wherein the instructions, when executed by the one or more processors individually or collectively, cause the wearable electronic device to obtain biometric data related to the blood flow of the user via the biosensor while an application related to a muscular exercise is executed, identify a grip strength change based on a change in the blood flow observed in the biometric data resulting from the muscular exercise performed by the user, create exercise information related to the muscular exercise based on the grip strength change, and provide the user with the exercise information via at least one of the communication circuitry or the output interface.

A modular, multi-use dynamometer 10 having a body 100 with a connector 120, 140 at each end for releasable attachment of accessories, such as a pads, loops or hooks, or a grip strength tool. The dynamometer can measure both compression and tensile forces. The accessories may have an electronically readable element, such as NFC or RFID such that the dynamometer 10 knowns which accessories are attached and reacts accordingly. The dynamometer preferably also has an in built IMU to allow range of motion measurements and the like.