A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors.

Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients' motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perform HAR by means of raw data, thus avoiding time-demanding feature engineering operations. Most works targeting HAR with DL-based architectures have tested the workflow performance on data related to a separate execution of the tasks. Hence, a paucity in the literature has been found with regard to frameworks aimed at recognizing continuously executed motor actions. In this article, the authors present the design, development, and testing of a DL-based workflow targeting continuous human activity recognition (CHAR). The model was trained on the data recorded from ten healthy subjects and tested on eight different subjects. Despite the limited sample size, the authors claim the capability of the proposed framework to accurately classify motor actions within a feasible time, thus making it potentially useful in a clinical scenario.

Biomechanical Effects of Using a Passive Exoskeleton for the Upper Limb in Industrial Manufacturing Activities: A Pilot Study.

This study investigates the biomechanical impact of a passive Arm-Support Exoskeleton (ASE) on workers in wool textile processing. Eight workers, equipped with surface electrodes for electromyography (EMG) recording, performed three industrial tasks, with and without the exoskeleton. All tasks were performed in an upright stance involving repetitive upper limbs actions and overhead work, each presenting different physical demands in terms of cycle duration, load handling and percentage of cycle time with shoulder flexion over 80°. The use of ASE consistently lowered muscle activity in the anterior and medial deltoid compared to the free condition (reduction in signal Root Mean Square (RMS) -21.6% and -13.6%, respectively), while no difference was found for the Erector Spinae Longissimus (ESL) muscle. All workers reported complete satisfaction with the ASE effectiveness as rated on Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST), and 62% of the subjects rated the usability score as very high (>80 System Usability Scale (SUS)). The reduction in shoulder flexor muscle activity during the performance of industrial tasks is not correlated to the level of ergonomic risk involved. This preliminary study affirms the potential adoption of ASE as support for repetitive activities in wool textile processing, emphasizing its efficacy in reducing shoulder muscle activity. Positive worker acceptance and intention to use ASE supports its broader adoption as a preventive tool in the occupational sector.

Measuring Surface Electromyography with Textile Electrodes in a Smart Leg Sleeve.

This paper presents the design, development, and validation of a novel e-textile leg sleeve for non-invasive Surface Electromyography (sEMG) monitoring. This wearable device incorporates e-textile sensors for sEMG signal acquisition from the lower limb muscles, specifically the anterior tibialis and lateral gastrocnemius. Validation was conducted by performing a comparative study with eleven healthy volunteers to evaluate the performance of the e-textile sleeve in acquiring sEMG signals compared to traditional Ag/AgCl electrodes. The results demonstrated strong agreement between the e-textile and conventional methods in measuring descriptive metrics of the signals, including area, power, mean, and root mean square. The paired data t-test did not reveal any statistically significant differences, and the Bland-Altman analysis indicated negligible bias between the measures recorded using the two methods. In addition, this study evaluated the wearability and comfort of the e-textile sleeve using the Comfort Rating Scale (CRS). Overall, the scores confirmed that the proposed device is highly wearable and comfortable, highlighting its suitability for everyday use in patient care.

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson's Disease.

The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson's disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson's disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson's disease.

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning.

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity.

A multiple linear regression approach to extimate lifted load from features extracted from inertial data.

SUMMARY: Work-related musculoskeletal disorders are among the main occupational health problems. Substantial evidence has shown that work-related physical risk factors are the main source of low back complaints, particularly affecting heavy and repetitive manual lifting activities. The aim of the study is, during load lifting tasks, to explore the correlation between the time domain features extracted from the acceleration and angular velocity signals of the performing subject and the load lifted, and to explore the feasibility of a multiple linear regression model to predict the lifted load. The acceleration and angular velocity signals were acquired along the three directions of space by means of an inertial sensor placed on the subject's chest, during lifting activities with load gradually increased by 1 kg from 0 kg to 18 kg. Successively three time-domain features (Root Mean Square, Standard Deviation and MinMax value) were extracted from the acquired signals. First a correlation analysis was carried out between each individual feature and the load lifted (calculating r); then the time-domain features that proved most representative (strong correlation) were used to create a multiple linear regression model (calculating R-square). The statistical analysis was carried out by means of the Pearson correlation and multiple linear regression model was fed with the most informative time-domain features according to the correlation analysis. The correlation analysis showed a strong correlation (r > 0,7) between six features (three extracted from z-axes acceleration and three extracted from y-axes angular velocity) and the lifted load. The predictive multiple linear regression model, fed with these six features achieved a Rsquare greater than 0,9.The study demonstrated that the proposed combination of kinematic features and a multiple regression model represents a valid approach to automatically calculate the load lifted based on raw signals obtained by means of an inertial sensor placed on the chest. The results confirm the potential application of this methodology to indirectly monitor the load lifted by workers during their activity.

Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment.

This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient's clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The "wearability" of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness.

Validity and reliability of textile system Sensoria for posturographic measurements.

OBJECTIVES: Smart fabrics and interactive textiles are a relatively new area of research, with many potential applications in the field of biomedical engineering. The ability of smart textiles to interact with the body provides a novel means to sense the wearer's physiology and respond to the needs of the wearer. Physiological signals, such as heart rate, breathing rates, and activity levels, are useful indicators of health status. These signals can be measured by means of textile-based sensors integrated into smart clothing which has the ability to keep a digital record of the patient's physiological responses since his or her last clinical visit, allowing doctors to make a more accurate diagnosis. Similarly, in rehabilitation, it is difficult for therapists to ensure that patients are complying with prescribed exercises. Smart garments sensing body movements have the potential to guide wearers through their exercises, while also recording their individual movements and adherence to their prescribed programme. METHODS: In this paper, we present the new wireless textile system Sensoria, with pressure sensing capability for static posturography. The gold standard for static posturography is currently the use of a pressure or force plate but, due to their very complexity and expensiveness, the applicability outside laboratories is extremely limited. RESULTS: This paper focuses on the agreement between the static computed posturography assessed by means of a traditional stabilometric platform and the Sensoria system, in twenty subjects with Parkinson's Disease (PD). CONCLUSIONS: Preliminary results showed a significant agreement between the two methods, suggesting a clinical use of Sensoria for low cost home care based balance impairment assessments.

Analysis of reaching movements of upper arm in robot assisted exercises. Kinematic assessment of robot assisted upper arm reaching single-joint movements.

Robot-mediated therapy (RMT) has been a very dynamic area of research in recent years. Robotics devices are in fact capable to quantify the performances of a rehabilitation task in treatments of several disorders of the arm and the shoulder of various central and peripheral etiology. Different systems for robot-aided neuro-rehabilitation are available for upper limb rehabilitation but the biomechanical parameters proposed until today, to evaluate the quality of the movement, are related to the specific robot used and to the type of exercise performed. Besides, none study indicated a standardized quantitative evaluation of robot assisted upper arm reaching movements, so the RMT is still far to be considered a standardised tool. In this paper a quantitative kinematic assessment of robot assisted upper arm reaching movements, considering also the effect of gravity on the quality of the movements, is proposed. We studied a group of 10 healthy subjects and results indicate that our advised protocol can be useful for characterising normal pattern in reaching movements.

Prognostic decision support using symbolic dynamics in CTG monitoring.

Foetal heart rate variability is one of the most important parameters to monitor foetal wellbeing. Linear parameters, widely employed to study foetal heart variability, have shown some limitations in highlight dynamics potentially relevant. During the last decades, therefore, nonlinear analysis methods have gained a growing interest to analyze the chaotic nature of cardiac activity. Parameters derived by techniques investigating nonlinear can be included in computerised systems of cardiotocographic monitoring. In this work, we described an application of symbolic dynamics to analyze foetal heart rate variability in healthy foetuses and a concise index, introduced for its classification in antepartum CTG monitoring. The introduced index demonstrated to be capable to highlight differences in heart rate variability and resulted correlated with the Apgar score at birth, in particular, higher variability indexes values are associated to early greater vitality at birth. These preliminary results confirm that SD can be a helpful tool in CTG monitoring, supporting medical decisions in order to assure the maximum well-being of newborns.

Diagnostic decision support of heart rate turbulence in sleep apnea syndrome.

Obstructive sleep apnea syndrome (OSAS) is characterized by repeated upper-airway obstruction during sleep. It is diagnosed by polysomnographic studies, scoring OSAS severity by an apneas/hypopneas index associated to worse prognosis, mainly for an increased cardiovascular morbidity. Cardiac autonomic impairments involved in the development of cardiovascular disease in OSAS can be assessed by heart rate turbulence (HRT) analysis and aim of the paper is to show the increased medical decision support by HRT evaluation in OSAS patients. HRT has been assessed in 274 polysomnographic recordings of mild-to-severe OSAS patients and an overall cardiorespiratory risk scoring (CRRIS) index has been proposed on the base of both OSAS severity and HRT assessment. Results showed that, while the only polysomnografic analysis would have equally ranked OSAS patients within their mild-to-severe classification, CRRIS index allows to identify a 19% of severe-OSAS patients at very high risk of sudden cardiac death, a 13% of moderate-OSAS patients with a risk level comparable to those of severe, and a 17% of mild-OSAS patients with evidence of an autonomic impairment. CRRIS index, detecting patients at greater probability of worsening could give to the physician a very useful medical decision support in the follow up of this particular chronic disease.

Gait asymmetry in stroke patients with unilateral spatial neglect.

The recovery of independent gait represents one of the main functional goals of the rehabilitative interventions after stroke but it can be hindered by the presence of unilateral spatial neglect (USN). The aim of the paper is to study if the presence of USN in stroke patients affects lower limb gait parameters between the two body sides, differently from what could be expected by the motor impairment alone, and to explore whether USN is associated to specific gait asymmetry. Thirty-five stroke patients (right or left lesion and ischemic or hemorrhagic etiology) who regained independent gait were assessed for global cognitive functioning and USN. All patients underwent a gait analysis session by using a wearable inertial system, kinematic parameters were computed. Enrolled patients presented altered motion parameters. Stroke patients with USN showed specific asymmetries in the following parameters: stance phase, swing phase, and knee range of motion. No differences in the clinical scores were found as the presence of USN. The presence of USN was associated with a specific form of altered gait symmetry. These findings may help clinicians to develop more tailored rehabilitative training to enhance gait efficacy of patients with motor defects complicated by the presence of selected cognitive impairments. Overview of the experiment setup. The workflow shows: diagnosis of unilateral spatial neglect by the neuropsychologist, sensors placement, gait analysis protocol and evaluation of the gait asymmetry together with the statistically significant features.

Reliability of IMU-Derived Gait Parameters in Foot Drop Patients.

Foot drop is a deficit in foot dorsiflexion causing difficulties in walking. Passive ankle-foot orthoses are external devices used to support the drop foot improving gait functions. Foot drop deficits and therapeutic effects of AFO can be highlighted using gait analysis. This study reports values of the major spatiotemporal gait parameters assessed using wearable inertial sensors on a group of 25 subjects suffering from unilateral foot drop. Collected data were used to assess the test-retest reliability by means of Intraclass Correlation Coefficient and Minimum Detectable Change. Excellent test-retest reliability was found for all the parameters in all walking conditions. The analysis of Minimum Detectable Change identified the gait phases duration and the cadence as the most appropriate parameters to detect changes or improvements in subject gait after rehabilitation or specific treatment.

Effects of Ankle-Foot Orthosis on Balance of Foot Drop Patients.

Ankle-Foot Orthoses (AFOs) are common non-surgical treatments used to support foot and ankle joint when their normal functioning is compromised. AFOs have relevant impact on gait biomechanics, while scientific literature about effects on static balance is less strong and confusing. This study aims to assess the effectiveness of a plastic semi-rigid AFO in improving static balance on foot drop patients. Results underline that no significant effects on static balance is obtained on the study population when the AFO is used on the impaired foot.

Using Features Extracted from Upper Limb Reaching Tasks to Detect Parkinson's Disease by means of Machine Learning Models.

While in the literature there is much interest in investigating lower limbs gait of patients affected by neurological diseases, such as Parkinson's Disease (PD), fewer publications involving upper limbs movements are available. In previous studies, 24 motion signals (the so-called reaching tasks) of the upper limbs of PD patients and Healthy Controls (HCs) were used to extract several kinematic features through a custom-made software; conversely, the aim of our paper is to investigate the possibility to build models - using these features - for distinguishing PD patients from HCs. First, a binary logistic regression and, then, a Machine Learning (ML) analysis was performed by implementing five algorithms through the Knime Analytics Platform. The ML analysis was performed twice: first, a leave-one out-cross validation was applied; then, a wrapper feature selection method was implemented to identify the best subset of features that could maximize the accuracy. The binary logistic regression achieved an accuracy of 90.5%, demonstrating the importance of the maximum jerk during subjects upper limb motion; the Hosmer-Lemeshow test supported the validity of this model (p-value=0.408). The first ML analysis achieved high evaluation metrics by overcoming 95% of accuracy; the second ML analysis achieved a perfect classification with 100% of both accuracy and area under the curve receiver operating characteristics. The top-five features in terms of importance were the maximum acceleration, smoothness, duration, maximum jerk and kurtosis. The investigation carried out in our work has proved the predictive power of the features, extracted from the reaching tasks involving the upper limbs, to distinguish HCs and PD patients.

Ultradian rhythms during day and night in normal and COPD subjects.

The analysis of heart rate variability (HRV) is a powerful tool in the study of the autonomic control of the heart. While circadian HRV rhythms have widely been characterized by traditional spectral measures, ultradian oscillations are not commonly investigated. In this study the identification of HRV ultradian rhythms is assigned to a quantitative measure characterizing the fractal-like behavior of the time series: the fractal dimension (FD). In order to assess ultradian regulation in Chronic Obstructive Pulmonary Disease (COPD) 24-h Holter ECG recordings of 52 COPD and 10 normal (healthy) subjects were analyzed. The FD was calculated by Higuchi's algorithm both during daytime and nighttime to highlight possible wake-sleep states differences. All subjects showed a similar common rhythm (0.06mHz) that persists with generally higher amplitude during night-time. A further rhythm becomes predominant in normal subjects in the day-to-night transition (0.15mHz), probably under the influence of the REM/non-REM ultradian sleep cycle. A very large difference between night and day amplitudes of this rhythm and of the next one (at about 0.22mHz) characterize the HRV fractal dimension of the Normal in respect of COPD. In conclusion, the FD could be used as a marker of ultradian cardiac autonomic regulation providing new insights into autonomic physiology of normal and COPD patients.

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks.

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject's sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate "risk" and "no risk" NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model-fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum-is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios).

A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients.

BACKGROUND: After the acute disease, convalescent coronavirus disease 2019 (COVID-19) patients may experience several persistent manifestations that require multidisciplinary pulmonary rehabilitation (PR). By using a machine learning (ML) approach, we aimed to evaluate the clinical characteristics predicting the effectiveness of PR, expressed by an improved performance at the 6-min walking test (6MWT). METHODS: Convalescent COVID-19 patients referring to a Pulmonary Rehabilitation Unit were consecutively screened. The 6MWT performance was partitioned into three classes, corresponding to different degrees of improvement (low, medium, and high) following PR. A multiclass supervised classification learning was performed with random forest (RF), adaptive boosting (ADA-B), and gradient boosting (GB), as well as tree-based and k-nearest neighbors (KNN) as instance-based algorithms. RESULTS: To train and validate our model, we included 189 convalescent COVID-19 patients (74.1% males, mean age 59.7 years). RF obtained the best results in terms of accuracy (83.7%), sensitivity (84.0%), and area under the ROC curve (94.5%), while ADA-B reached the highest specificity (92.7%). CONCLUSIONS: Our model enables a good performance in predicting the rehabilitation outcome in convalescent COVID-19 patients.

A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting.

BACKGROUND: One of the main problems in poorly controlled asthma is the access to the Emergency Department (ED). Using a machine learning (ML) approach, the aim of our study was to identify the main predictors of severe asthma exacerbations requiring hospital admission. METHODS: Consecutive patients with asthma exacerbation were screened for inclusion within 48 hours of ED discharge. A k-means clustering algorithm was implemented to evaluate a potential distinction of different phenotypes. K-Nearest Neighbor (KNN) as instance-based algorithm and Random Forest (RF) as tree-based algorithm were implemented in order to classify patients, based on the presence of at least one additional access to the ED in the previous 12 months. RESULTS: To train our model, we included 260 patients (31.5% males, mean age 47.6 years). Unsupervised ML identified two groups, based on eosinophil count. A total of 86 patients with eosinophiles ≥370 cells/µL were significantly older, had a longer disease duration, more restrictions to daily activities, and lower rate of treatment compared to 174 patients with eosinophiles <370 cells/µL. In addition, they reported lower values of predicted FEV1 (64.8±12.3% vs. 83.9±17.3%) and FEV1/FVC (71.3±9.3 vs. 78.5±6.8), with a higher amount of exacerbations/year. In supervised ML, KNN achieved the best performance in identifying frequent exacerbators (AUROC: 96.7%), confirming the importance of spirometry parameters and eosinophil count, along with the number of prior exacerbations and other clinical and demographic variables. CONCLUSIONS: This study confirms the key prognostic value of eosinophiles in asthma, suggesting the usefulness of ML in defining biological pathways that can help plan personalized pharmacological and rehabilitation strategies.

Machine learning can predict mild cognitive impairment in Parkinson's disease.

Background: Clinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-ß42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-ß (Aß) failed to consistently demonstrate the association between Aß plaques deposition and mild cognitive impairment in PD (PD-MCI). Aim: Finding significant features associated with PD-MCI through a machine learning approach. Patients and methods: Patients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model. Results: Seventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2. Conclusions: This study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.