Vito Errico

Sensory gloves are devices capable of measuring finger movements and are useful in numerous applications, many of which require real-time data acquisition. However, the procedures explored in the literature to assess measurement repeatability and reliability mainly rely on static or quasi-static conditions. To overcome this limitation, here we propose a testing procedure for assessing measurements under dynamic conditions (slow, medium, and rapid finger joint movements). To this aim, we used two sensory gloves, one based on resistive flex sensors (RFSs) and another based on inertial measurement units (IMUs) – as two of the most adopted types. Our study demonstrated the feasibility of measuring dynamic finger movements and the differences in dynamic measurement repeatability and reliability between RFS- and IMU-based gloves when considering the angles (in degrees) of each finger joint. The RFS-based glove scored with an average range ± standard deviation (SD) of 6.84 ± 2.77°, and an intraclass correlation coefficient (ICC) of 0.77 ± 0.14, whereas the IMU-based glove scored with an average range of 8.49 ± 2.72°, and an ICC of 0.75 ± 0.14. Both gloves exhibited better repeatability and reliability at the slowest speed, with the RFS-based glove having an higher repeatability than the IMU-based one ( p < 0.001). Moreover, when compared to previous studies, the results (in terms of reliability and repeatability) here obtained under dynamic conditions are comparable to those obtained under static or quasi-static conditions. In summary, our results indicate that both proposed sensory gloves are suitable for most applications that require dynamic interactions.

Objective: In order to evaluate Parkinson disease patients response to therapeutic interventions, sources of information are mainly patient reports and clinicians assessment of motor functions. However, these sources can suffer from patients subjectivity and from inter/intra raters score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment. Methods: Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification. Results: According to our findings, levodopa-based therapy alters the patients conditions in general, ameliorating something (e.g. bradykinesia), leaving unchanged others (e.g. tremor), but with poor correlation to the levodopa dose. Conclusion: A technology-based approach can objectively assess levodopa-based therapy effectiveness. Significance: Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports.

Arm-and-hand tracking by technological means allows gathering data that can be elaborated for determining gesture meaning. To this aim, machine learning (ML) algorithms have been mostly investigated looking for a balance between the highest recognition rate and the lowest recognition time. However, this balance comes mainly from statistical models, which are challenging to interpret. In contrast, we present μ C¹ and μ C², two geometric model-based approaches to gesture recognition which support the visualization and geometrical interpretation of the recognition process. We compare μ C¹ and μ C² with respect to two classical ML algorithms, k-nearest neighbor (k-NN) and support vector machine (SVM), and two state-of-the-art (SotA) deep learning (DL) models, bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU), on an experimental dataset of ten gesture classes from the Italian Sign Language (LIS), each repeated 100 times by five inexperienced non-native signers, and gathered with wearable technology (a sensory glove and inertial measurement units). As a result, we achieve a compromise between high recognition rates (>90%) and low recognition times (<0.1 s) that is adequate for human-computer interaction. Moreover, we elaborate on the algorithms' geometric interpretation based on geometric algebra, which supports some understanding of the recognition process.

Sign languages (SL) can aid in improving the communication between deaf and hearing communities, but only a limited number of hearing individuals understand it. SL recognition systems tackle this issue by using sensors to acquire gesture data, later converted into spoken or written language by means of machine learning algorithms. Within this frame, we propose a wearable electronic-based system for achieving automatic signer-independent recognition of Italian SL. The system is used for recognizing ten signs performed by 17 inexperienced signers. The wearable electronics measure hand, arm, and forearm gestures, by means of a sensory glove (equipped with ten flex sensors) and six Inertial Measurement Units (IMUs). We adopt three classifiers: Artificial Neural Network (ANN), k-Nearest Neighbors and Support Vector Machine, with ANN outperforming the others with an accuracy of 95.07%. However, the real key and novel aspect of this work concerns exploring the importance of each sensor for the overall classification accuracy, with the aim of determining the minimum number of sensors (and so minimizing the complexity of the wearable system) necessary to guarantee an acceptable accuracy. As a result, we could drop out three of the six IMUs, obtaining an accuracy as high as 93.91%. In addition, this valuable result was obtained using a signer-independent approach, which makes the application closer to a real-life situation.

We realized and tested a novel system aimed at discriminating different hand poses by means of an active actuating and sensing approach realized by converting electromagnetic to mechanical waves (and vice versa) and analyzing the characteristics of the waves after their travel through the bones and cartilage of the hand. The actuating part is realized through a transducer, placed on the dorsal part of the hand, performing electromagnetic-to-mechanical energy conversion. The sensing part is realized through an accelerometer, placed on a finger, performing mechanical-to-electromagnetic energy conversion. The way the sound propagates through the fingers mainly depends on the traveled path, the angles of fingers’ flexion, and the (un)matched conditions with surroundings. Therefore, the investigation of the characteristics of the returned signal (in phase shifts and power spectral densities) can furnish information for discriminating among different finger poses and different densities of touched materials. The system highly performed in repeatability and reproducibility of the measures, well-discriminating among hand poses and among densities of touched materials.

Myotonic dystrophy type 1 (DM1) is a genetic inherited autosomal dominant disease characterized by multisystem involvement, including muscle, heart, brain, eye, and endocrine system. Although several methods are available to evaluate muscle strength, endurance, and dexterity, there are no validated outcome measures aimed at objectively evaluating qualitative and quantitative gait alterations. Advantageously, wearable sensing technology has been successfully adopted in objectifying the assessment of motor disabilities in different medical occurrences, so that here we consider the adoption of such technology specifically for DM1. In particular, we measured motor tasks through inertial measurement units on a cohort of 13 DM1 patients and 11 healthy control counterparts. The motor tasks consisted of 16 meters of walking both at a comfortable speed and fast pace. Measured data consisted of plantar-flexion and dorsi-flexion angles assumed by both ankles, so to objectively evidence the footdrop behavior of the DM1 disease, and to define a novel severity index, termed SI-Norm2, to rate the grade of walking impairments. According to the obtained results, our approach could be useful for a more precise stratification of DM1 patients, providing a new tool for a personalized rehabilitation approach.