Publications

Hereditary spastic paraplegia (HSP) is characterised by progressive lower-limb spasticity and weakness resulting in ambulation difficulties. During clinical practice, walking is observed and/or assessed by timed 10-metre walk tests; time, feasibility, and methodological reliability are barriers to detailed characterisation of patients’ walking abilities when instrumenting this test. Wearable sensors have the potential to overcome such drawbacks once a validated approach is available for patients with HSP. Therefore, while limiting patients’ and assessors’ burdens, this study aims to validate the adoption of a single lower-back wearable inertial sensor approach for step detection in HSP patients; this is the first essential algorithmic step in quantifying most gait temporal metrics. After filtering the 3D acceleration signal based on its smoothness and enhancing the step-related peaks, initial contacts (ICs) were identified as positive zero-crossings of the processed signal. The proposed approach was validated on thirteen individuals with HSP while they performed three 10-metre tests and wore pressure insoles used as a gold standard. Overall, the single-sensor approach detected 794 ICs (87% correctly identified) with high accuracy (median absolute errors (mae): 0.05 s) and excellent reliability (ICC = 1.00). Although about 12% of the ICs were missed and the use of walking aids introduced extra ICs, a minor impact was observed on the step time quantifications (mae 0.03 s (5.1%), ICC = 0.89); the use of walking aids caused no significant differences in the average step time quantifications. Therefore, the proposed single-sensor approach provides a reliable methodology for step identification in HSP, augmenting the gait information that can be accurately and objectively extracted from patients with HSP during their clinical assessment.

Augmented Reality (AR) can improve the accuracy of surgical procedures by displaying 3D reconstructions of organs directly on the patient’s body. Moreover, it can improve the surgeon’s level of attention by freeing him from the task of mentally associating information from various sources. A crucial aspect for the reliability of an AR system is the registration process, which aims to guarantee the correspondence between the 3D model and the real organ during the whole operation. The research work introduced in this paper aims to improve the accuracy of the registration process by updating the alignment between real and virtual organs even in presence of deformations induced by the insertion of surgical instruments or small displacements that organs might undergo during surgery. By detecting even imperceptible movements, the inertial sensors allow dynamic updating of the alignment between real and virtual organs to ensure an error of a few millimetres. The solution introduced in this study could therefore constitute a first step towards improving precision in AR-guided surgery, although more in-depth studies are needed to assess the optimal positioning of the sensors, which in the future could be done automatically by an application that suggests it in the preparatory phase of surgery.

Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors).

Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (SDA, including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-hours of real-world unsupervised activity. Excellent absolute agreement (ICC > 0.95) and very limited mean absolute errors were observed for all cohorts and DMOs (cadence ≤ 0.61 steps/min, stride length ≤ 0.02 m, walking speed ≤ 0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the SDA (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-hours acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

This thesis proposed the methods to accurately estimate the human joint kinematics starting from the MIMU signals. Three main contributions were provided. The first consisted in the design of a comprehensive battery of tests to completely characterize the sources of errors affecting the quality of the measurements. These tests rely on simple hypotheses based on the sensor working principles and do not require expensive equipment. Nine parameters were defined to quantify the signal accuracy improvements (if any) of 24 MIMUs before and after the refinement of their calibration coefficients. The second contribution was focused on the SFAs. Ten among the most popular SFAs were compared under different experimental conditions including different MIMU models and rotation rate magnitudes.

Thanks to continuous advancements in sensor miniaturization, it has become feasible to capture data related to various aspects of human motion, including acceleration, angular velocity, and foot pressure. In the last few years, these technologies have been mainly used for automatic activity classification. In this work, we developed a non-invasive method for automatically categorize postures based on inertial sensors and foot plantar pressure data obtained from a wearable systems. In a controlled office environment, four healthy participants performed two types of lifting movements. The primary objective was to utilize this system to identify and classify awkward postures that could potentially pose injury risks to warehouse workers. An artificial neural network, a supervised machine learning ,was used for the classification, achieving an accuracy rate exceeding 80\%. This research demonstrate the possibility of employing wearable IMU (Inertial Measurement Unit) and insole pressure systems to distinguish between different human activities and exploit these findings to mitigate the exposure to potentially dangerous postures.

Thanks to continuous advancements in sensor miniaturization, it has become feasible to capture data related to various aspects of human motion, including acceleration, angular velocity, and foot pressure. In the last few years, these technologies have been mainly used for automatic activity classification. In this work, we developed a non-invasive method for automatically categorize postures based on inertial sensors and foot plantar pressure data obtained from a wearable systems. In a controlled office environment, four healthy participants performed two types of lifting movements. The primary objective was to utilize this system to identify and classify awkward postures that could potentially pose injury risks to warehouse workers. An artificial neural network, a supervised machine learning ,was used for the classification, achieving an accuracy rate exceeding 80\%. This research demonstrate the possibility of employing wearable IMU (Inertial Measurement Unit) and insole pressure systems to distinguish between different human activities and exploit these findings to mitigate the exposure to potentially dangerous postures.

The accurate identification of initial and final foot contacts is a crucial prerequisite for obtaining a reliable estimation of spatio-temporal parameters of gait. Well-accepted gold standard techniques in this field are force platforms and instrumented walkways, which provide a direct measure of the foot–ground reaction forces. Nonetheless, these tools are expensive, non-portable and restrict the analysis to laboratory settings. Instrumented insoles with a reduced number of pressure sensing elements might overcome these limitations, but a suitable method for gait events identification has not been adopted yet. The aim of this paper was to present and validate a method aiming at filling such void, as applied to a system including two insoles with 16 pressure sensing elements (element area = 310 mm2), sampling at 100 Hz. Gait events were identified exploiting the sensor redundancy and a cluster-based strategy. The method was tested in the laboratory against force platforms on nine healthy subjects for a total of 801 initial and final contacts. Initial and final contacts were detected with low average errors of (about 20 ms and 10 ms, respectively). Similarly, the errors in estimating stance duration and step duration averaged 20 ms and <10 ms, respectively. By selecting appropriate thresholds, the method may be easily applied to other pressure insoles featuring similar requirements.

Stride length is often used to quantitatively evaluate human locomotion performance. Stride by stride estimation can be conveniently obtained from the signals recorded using miniaturized inertial sensors attached to the feet and appropriate algorithms for data fusion and integration. To reduce the detrimental drift effect, different algorithmic solutions can be implemented. However, the overall method accuracy is supposed to depend on the optimal selection of the parameters which are required to be set. This study aimed at evaluating the influence of the main parameters involved in well-established methods for stride length estimation. An optimization process was conducted to improve methods’ performance and preferable values for the considered parameters according to different walking speed ranges are suggested. A parametric solution is also proposed to target the methods on specific subjects’ gait characteristics. The stride length estimates were obtained from straight walking trials of five healthy volunteers and were compared with those obtained from a stereo-photogrammetric system. After parameters tuning, percentage errors for stride length were 1.9%, 2.5% and 2.6% for comfortable, slow, and fast walking conditions, respectively.

Telerehabilitation is a branch of telemedicine that aims to rehabilitate the patient from remote, with the help of ICT solutions. By bringing the rehabilitation tools to the patients’ home, providing ways to control their progress and correct their errors, telerehabilitation systems promote fair access to the treatments also for those patients who live far from the rehabilitation centers or present limited autonomy in reaching them. As telerehabilitation limits the real-time interaction with the rehabilitation staff and the exercises are usually repetitive and boring, the main risk is the reduction of the compliance to the prescribed protocol. In long-term rehabilitation situations, this often leads to dropouts, thus limiting the recovery. To avoid such problems, an interesting development is the use of playful tools, such as video games, for rehabilitation purposes. These tools are called exergames because they help the patient to exercise in a gamified scenario. Although exergame-based rehabilitation systems are moving from the research to the market, the combined use of low-cost platforms, telerehabilitation based on quantitative measurements of biomechanical parameters, and exergames is still seen as the holy grail in the field. This is due to the complexity of these systems…

Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users’ perspective on the device.

Condition Monitoring is the process of monitoring a parameter of a particular machine, for the purpose of identifying developing anomalies. In this thesis, in cooperation with 221e S.r.l. during an internship, an autoencoder-based Condition Monitoring system is proposed, with the aim of detecting anomalies in machines using sound signals. Sound indicators in Condition Monitoring offer multiple advantages over more traditional metrics like temperature, vibration, or voltage. Anomalies can be detected before major malfunctions occur, the machine can be monitored without physical contact and a large set of different anomalies can be detected. The system was developed and evaluated on three real user scenarios provided by the company. Different conditions and settings, with customized data acquisitions and training of the model, were considered. In the end, an embedding of the monitoring solution into the microcontroller multi-sensor board STWIN is considered and validated on the device.

Neuro-rehabilitation is a key treatment for motor skills recovery after a cerebrovascular stroke. Based on the level and size of the brain lesion, different degrees of motor impairment could arise. A typical problem of post-stroke patients is an alteration in the upright stance balance on the feet. For patients with mild to moderate impairment, home rehabilitation could provide substantial benefits in terms of functional recovery, without the discomfort and costs associated with a conventional rehabilitation in a care center. This work presents the development of a wearable m-health solution for biofeedback provisioning to the patient in balance recovery exercises. By exploiting two FSR-sensorized insoles and a custom readout electronics, an Android app can provide visual biofeedback and simple exergame to help the patient in the recovery of a correct distribution of plantar pressure. A test performed on ten healthy subjects reveals that the proposed system provides a reasonable distribution of the plantar pressure according to the current knowledge in the field. The proposed solution can be easily integrated into a software framework for home telerehabilitation.

Neuro-rehabilitation is a key treatment for motor skills recovery after a cerebrovascular stroke. Based on the level and size of the brain lesion, different degrees of motor impairment could arise. A typical problem of post-stroke patients is an alteration in the upright stance balance on the feet. For patients with mild to moderate impairment, home rehabilitation could provide substantial benefits in terms of functional recovery, without the discomfort and costs associated with a conventional rehabilitation in a care center. This work presents the development of a wearable m-health solution for biofeedback provisioning to the patient in balance recovery exercises. By exploiting two FSR-sensorized insoles and a custom readout electronics, an Android app can provide visual biofeedback and simple exergame to help the patient in the recovery of a correct distribution of plantar pressure. A test performed on ten healthy subjects reveals that the proposed system provides a reasonable distribution of the plantar pressure according to the current knowledge in the field. The proposed solution can be easily integrated into a software framework for home telerehabilitation.

In this paper, a wearable electronic platform for in-ear photoplethysmography is presented. The system was specifically designed with a miniaturized form factor in order to enable the potential monitoring of athletes during physical activity, as well professionals wearing earbuds or headphones during their normal duties. Measurements have been collected to address the main factors affecting the signal quality: as a result, the fine tuning of the configuration parameters allowed the extension of the battery lifetime of the system to over 3 days of continuous monitoring. Moreover, an investigation of the best body measurement location has been carried out.

In this paper, a wearable electronic platform for in-ear photoplethysmography is presented. The system is specifically designed with a miniaturized form factor in order to enable the potential monitoring of athletes during physical activity, as well as professionals wearing earbuds or headphones during their normal duties. Measurements have been collected to address the main factors affecting the signal quality. As a result, the fine tuning of the configuration parameters allowed the extension of the battery lifetime of the system to over 3 days of continuous operation. Moreover, an investigation of the best body measurement location has been carried out. Two different algorithms have been developed: the former is a lightweight procedure for heart rate (HR) estimation suitable for embedded implementation whereas the latter features a motion mitigation adaptive filter to compensate the effect of motion artifacts (MAs).

Le recenti esperienze (terremoti de L’Aquila, 2009, Emilia-Romagna, 2012, Centro Italia, 2016) hanno evidenziato come, nelle fasi di soccorso a seguito dell’evento sismico, l’organizzazione delle operazioni di intervento assuma un’importanza fondamentale. Tali operazioni potrebbero essere facilitate nel caso in cui parte del patrimonio esistente disponesse di sensori in grado di fornire un primo feedback dello stato di salute dell’edificio. L’applicazione di queste tecnologie fornirebbe l’input per effettuare una stima dei danni mediante indici già presenti in letteratura. Oltre a fornire alle autorità competenti uno strumento utile per le operazioni di soccorso, tali informazioni potrebbero essere di supporto per la valutazione ì dell’agibilità post-sisma e per la conseguente definizione degli interventi di riparazione. Tuttavia, per rendere fattibile l’installazione di questa tecnologia spesso si opta per l’utilizzo di accelerometri basati su tecnologia MEMS (Micro Electro-Mechanical Systems) a costo contenuto, caratterizzati da un rumore strumentale che potrebbe inficiarne l’applicazione. Il presente lavoro si propone di valutare l’applicabilità di questi sistemi di misurazione e di valutarne l’accuratezza a partire dalla loro implementazione in indici di danno in letteratura e da prove su tavola vibrante, validando le procedure implementate negli algoritmi di calcolo attraverso l’applicazione a un caso studio.

This work is concerned with the development of a wireless low-power wearable system to be used for multi-lead ECG monitoring. Potential applications can range from sport and fitness to healthcare.Query The paper aims to present the architecture of the system and its performance, along with in vivo results achieved with carbon based smart textiles.

The role of the telemedicine is becoming more relevant in the last years with the increasing ageing of the population. This paper aims to describe a telemedicine-oriented system developed for the assessment of a person’s mobility during the Extended Timed Up and Go (ETUG) test. A wireless sensor network composed of 3 inertial platforms was mounted on the subject’s chest and legs and recorded the data during the execution of the test. Significant motor features were extracted by means of different algorithms, which were later integrated in a cloud-based Android application. Preliminary results show the capability of the system to identify the assessment features, allowing the medical staff to properly evaluate the physical performances of their patients.

This work reports advances in tremor classification based on a wireless wearable sensor presented by the authors in a previous paper. This device was characterized in order to determine the reliability of the platform when used as a frequency measurement unit. Results confirm the system accuracy in the estimation of the dominant frequency of the measured vibrations. In addition, device’s orientations, accelerations and angular speeds were collected from 15 patients affected by parkinsonian tremor during the execution of four standardized tasks: those data were analyzed in order to assess the equivalence of the results obtained from different data sources. The procedure highlights both a strong correspondence between the different data sources in the task that emphasizes the parkinsonian tremor, and similar trends in the results.

This work presents methodologies and techniques for the design of wearable sensor systems to be used for the measurement of physiological and environmental parameters. Such devices are becoming widespread in healthcare, sport and fitness, and are taking part to the Internet-of-Things revolution. The paper aims to describe some of the challenges faced at the design stage and possible solutions to be adopted.

This thesis presents the results of the development of wireless systems based on advanced sensor platforms and algorithms, for the remote monitoring of patients affected by central nervous system (CNS) diseases. Central nervous system diseases are a broad category of neurological disorders affecting the structure or the functionality of the brain or spinal cord. Such conditions may be an inherited metabolic disorder; the result of damage from an infection, a degenerative condition, stroke, a brain tumor or other problems; or arise from unknown or multiple factors. Several types of CNS diseases exist…

The technical documentation, or more precisely the technical le of a medical device, represents a document of extreme importance for allowing to conformity assessments and for obtaining the CE marking necessary at the commercialization of the device in the European market. If the technical le drawn up proves to be inadequate or incomplete, the entry of the device in the market will may be delayed or prohibited. The manufacture has the task of drafting the technical documentation, and then he is responsible for ensuring that all the information concerning the technical aspects, manufacture, production, clinical evaluation and safety of the device. It will be the duty of the manufacturer to rely on the
help of a person skilled in this eld: the biomedical engineer. The latter must comply with the provisions contained in the regulatory directives issued by the Council of the European Union. These provisions have the task to delinate…

XXI Congresso Annuale della SIAMOC, modalità telematica il 30 settembre e il 1° ottobre 2021. Come da tradizione, il congresso vuole essere un’occasione di arricchimento e mutuo scambio, dal punto di vista scientifico e umano. Verranno toccati i temi classici dell’analisi del movimento, come lo sviluppo e l’applicazione di metodi per lo studio del movimento nel contesto clinico, e temi invece estremamente attuali, come la teleriabilitazione e il telemonitoraggio.