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The present work focuses on the tapping test, which is a method that is commonly used in the literature to assess dexterity, speed, and motor coordination by repeatedly moving fingers, performing a tapping action on a flat surface. During the test, the activation of specific brain regions enhances fine motor abilities, improving motor control. The research also explores neuromuscular and biomechanical factors related to finger dexterity, revealing neuroplastic adaptation to repetitive movements. To give an objective evaluation of all cited physiological aspects, this work proposes a measurement architecture consisting of the following: (i) a novel measurement protocol to assess the coordinative and conditional capabilities of a population of participants; (ii) a suitable measurement platform, consisting of synchronized and non-invasive inertial sensors to be worn at finger level; (iii) a data analysis processing stage, able to provide the final user (medical doctor or training coach) with a plethora of useful information about the carried-out tests, going far beyond state-of-the-art results from classical tapping test examinations. Particularly, the proposed study underscores the importance interdigital autonomy for complex finger motions, despite the challenges posed by anatomical connections; this deepens our understanding of upper limb coordination and the impact of neuroplasticity, holding significance for motor abilities assessment, improvement, and therapeutic strategies to enhance finger precision. The proof-of-concept test is performed by considering a population of college students. The obtained results allow us to consider the proposed architecture to be valuable for many application scenarios, such as the ones related to neurodegenerative disease evolution monitoring.

The monitoring of coordinative abilities in sports applications is often carried out by trainers that adopt subjective protocols and evaluations not supported by repeatable and reproducible measurement setups. This often leads to unreliable evaluations that do not allow us to quantify the positive or negative effect of some training in a simple way. In this scenario, the rapid spreading of wearable devices able to capture human movements providing data to the users could be a useful instrument to face the problem of simplifying the development of automated, repeatable, and reproducible measurement procedures easily adoptable by a community of athletes or coaches. Following this path, the paper proposes an automatic measurement protocol for the assessment of coordinative abilities based on the use of IMUs embedded in wearable devices. A new protocol based on the ruler and tapping tests and a set of objective key performance indicators, derived from IMU measurements, to evaluate the outcome of the test is then developed. In detail, the protocol is based on the sequence 1) ruler test; 2) tapping test; 3) ruler test. The tapping test is performed until energy exhaustion to try and identify, from inertial data, features that can describe possible fatigue effects and correlations with reaction time. Ruler tests are adopted to evaluate the reaction time. The first ruler test provides reaction time information in rest conditions, while the last one considers it after a repeated movement. The comparison of these two times will show whether the reaction time changes after a fatigue condition. An algorithm capable of calculating the number of tapping and the reaction time of each subject is implemented to evaluate the accelerometric data acquired during the tests. Therefore, the impact of the work is two-fold: from an engineering point of view, the automation and performance evaluation of the proposed algorithm is provided; from a sport-medical perspective, the main finding is a general reduction of the reaction time after the energy-exhausting tapping test, as if this last could be considered as a powerful warm-up exercise for sports people. Two are the main results achieved: i) the proposed protocol allows a reduction of the reactions time in about the 83% of cases; ii) the proposed measurement system allows obtaining, regarding the tapping test, additional very useful quantities as the tap intertemps, frequency spectra, and acceleration excursions, which typically are not provided in state-of-the-art tapping test execution.

The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson’s disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator’s validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson’s disease tremor was greater than 98% in the best test conditions.