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)

Artificial neural network analyzing wearable device gait data for identifying patients with stroke unable to return to work

A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke

Hand rehabilitation with sonification techniques in the subacute stage of stroke

After a stroke event, most survivors suffer from arm paresis, poor motor control and other disabilities that make activities of daily living difficult, severely affecting quality of life and personal independence. This randomized controlled trial aimed at evaluating the efficacy of a music-based sonification approach on upper limbs motor functions, quality of life and pain perceived during rehabilitation. The study involved 65 subacute stroke individuals during inpatient rehabilitation allocated into 2 groups which underwent usual care dayweek) respectively of standard upper extremity motor rehabilitation or upper extremity treatment with sonification techniques. The Fugl-Meyer Upper Extremity Scale, Box and Block Test and the Modified Ashworth Scale were used to perform motor assessment and the McGill Quality of Life-it and the Numerical Pain Rating Scale to assess quality of life and pain. The assessment was performed at baseline, after 2weeks, at the end of treatment and at follow-up (1month after the end of treatment). Total scores of the Fugl-Meyer Upper Extremity Scale (primary outcome measure) and hand and wrist sub scores, manual dexterity scores of the affected and unaffected limb in the Box and Block Test, pain scores of the Numerical Pain Rating Scale (secondary outcomes measures) significantly improved in the sonification group compared to the standard of care group (time*group interaction<0.05). Our findings suggest that music-based sonification sessions can be considered an effective standardized intervention for the upper limb in subacute stroke rehabilitation

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

Job preservation by an office worker with idiopathic cervical dystonia: case report

Background: Work preservation is a main goal in the rehabilitation of chronic disabling diseases. We describe the application of an interdisciplinary protocol, involving the occupational therapist and the ergonomist, in the case of a 50 year-old office worker with idiopathic cervical dystonia, a movement disorder that can seriously impair work capability. Case report: The disease was diagnosed at age 25, and subsequently worsened. The man presented postural difficulties and pain. The symptomatology worsened during working shifts, preventing him from doing his job properly. Functional evaluation and ergonomic inspection of the office environment led to the correction of evident critical inadequacies. This allowed the patient to continue working in correct conditions, resulting in improvement of his global health status. Conclusions: The interdisciplinary rehabilitative approach here described may allow subjects with idiopathic cervical dystonia to keep their jobs by adapting the workplace to the changed physical capabilities

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

Esperienze di riabilitazione e ritorno al lavoro

Nonostante i progressi compiuti negli ultimi decenni in ambito preventivo (e le recenti diposizioni legislative in tema d’igiene e sicurezza degli ambienti di lavoro), gli infortuni sul lavoro rimangono uno dei principali problemi di salute pubblica. Dati INAIL mostrano una lieve riduzione del numero totale di infortuni negli ultimi anni, ma con valori assoluti ancora elevati. Gli infortuni sul lavoro spesso coinvolgono l’apparato locomotore, inducendo disabilità (temporanea o permanente) di varia entità (con elevati costi sociali), e richiedendo complessi trattamenti medico–chirurgici seguiti da riabilitazione specialistica. La ripresa lavorativa (uno dei principali obiettivi istituzionali della Fondazione Salvatore Maugeri) rappresenta la conclusione ottimale del programma riabilitativo. In letteratura esistono molti dati sulla ripresa lavorativa dopo infortunio professionale osteoarticolare; pochissimi studi, però, hanno valutato il ruolo del Medico del Lavoro nell’ottimizzare questo processo grazie ad un precoce intervento nel programma riabilitativo. Una ripresa soddisfacente e in tempo ottimale richiede infatti competenze proprie di questo specialista, in grado di valutare le caratteristiche della mansione (e i rischi ad essa correlati), e di valutarne l’adeguatezza alle condizioni globali di salute del singolo lavoratore. Per perseguire tale scopo, il nostro Istituto adotta un protocollo interdisciplinare applicabile a vittime di eventi traumatici a carico dell’apparato osteoarticolare. Questo capitolo descrive la metodologia e presenta i risultati ottenuti in due anni (2010–2011), fornendo principi guida per la valutazione iniziale e la precoce riabilitazione dei traumi osteoarticolari, focalizzandosi sul processo di ripresa lavorativa

Work resumption after occupational osteoarticular injury: interdisciplinary protocol and case record

We describe an interdisciplinary protocol for work resumption after serious occupational osteoarticular injury and subsequent rehabilitation. The outcomes obtained over two years are presented. Between 2010 and 2011, 159 injured patients (55 females, 104 males), aged between 19 and 75 years (average 44.8), were examined by the physiatrist and the occupational physician, to establish their functional capabilities and task features. The frequency, timing and modalities of work resumption were evaluated by telephonic follow-up, at 6 and 12 months after rehabilitation. The majority (66.7%) of the sample were manual workers, with moderate or heavy tasks. A fraction (8.8%) were healthcare operators. Injuries involved one or more of the following: upper limb (42.8%), lower limb (37.7%), cervical/dorsal spine (30.8%), lumbar/sacral spine (10.1%), other sites (10.1%). Associated peripheral nervous system damage was present in 15.1% of cases. Rehabilitation resulted in decreased pain and improved function. After 6 months, 116 out of 143 subjects available for follow-up (81.1%) had returned to work: 104 had returned to their previous tasks, on average after 14.6 days. Nine patients had changed job, 13 had partially changed the previous work tasks (average resumption time: 48.5 days), and 4 had resumed straightaway with temporary (< 3 months) limitations. After 12 months, 120 out of 141 (85.1%) subjects were working, 12 of them with limitations and/or partially changed tasks. The most relevant findings of the study are both the high percentage of patients returning to work (with few task changes), including those performing high-energy requirement jobs, and the short time required. The difficulty of work resumption for those who failed to restart work within 6 months indicates that the beneficial effect of rehabilitation is maximal in the short-medium period. This study highlights the importance of an interdisciplinary rehabilitative approach to facilitate work resumption after osteoarticular injury, adapting the work tasks to the changed physical capabilities

Disturbi muscolo-scheletrici e valutazioni di secondo livello per idoneità alla mansione: studio casistico

Introduzione. I disturbi muscolo-scheletrici (DMS) sono un ampio gruppo di affezioni dell’apparato locomotore, e delle strutture vascolari e nervose a esso afferenti. Caratterizzati da eziologia multifattoriale, essi sono spesso concausati dall’attività lavorativa. I DMS pongono delicati problemi diagnostici e medico-legali, comportano gravi conseguenze economiche e sociali, e generano problemi valutativi complessi in merito al giudizio d’idoneità alla mansione. Obiettivi. 1) Analisi della casistica recente, afferente al nostro Istituto, relativa a valutazioni di secondo livello (ex art. 39, comma 5 del D.lgs. 81/2008) per “idoneità difficili” in lavoratori affetti da DMS. 2) Identificazione di eventuali mutamenti temporali, mediante confronto con la casistica degli anni precedenti. Metodi. Tra le richieste di visita ex art. 39 afferite tra febbraio 2014 e aprile 2021, sono stati analizzati i DMS che hanno richiesto valutazione funzionale fisiatrica, raccogliendo –per ciascun paziente– i dati riguardanti diagnosi, distretti corporei interessati, mansioni svolte e indicazioni per l’idoneità alla mansione formulate al termine dell’iter valutativo. Risultati. Nel periodo considerato, sono state effettuate 235 valutazioni ex art. 39, delle quali 96 (40,9 %) per DMS con necessità di consulenza fisiatrica. Il campione così identificato (55 maschi e 41 femmine; età media: 49,4 anni) comprendeva 67 operai, 19 operatori sanitari, 8 impiegati e 2 autisti. Tali lavoratori in genere presentavano più di una patologia, talora con coinvolgimento di diversi distretti corporei. In totale sono state diagnosticate 153 patologie. Al termine dell’iter valutativo, 10 soggetti sono stati ritenuti non idonei alla mansione (in 4 casi temporaneamente), con indicazioni per un possibile ricollocamento. I restanti 86 sono stati valutati idonei, in 77 casi con limitazioni e/o prescrizioni, riguardanti, in 69, aspetti ergonomici e posturali: movimentazione manuale di carichi, movimenti ripetitivi, posture incongrue, posture fisse prolungate, deambulazione protratta, sollecitazioni biomeccaniche. Le altre limitazioni/prescrizioni erano relative a: pause durante il lavoro (n = 12), aspetti organizzativi (n = 9), vibrazioni (n = 8), turni notturni (n = 6), lavoro straordinario (n = 3), lavoro in quota (n = 3), guida di carrelli o mezzi aziendali (n = 4), microclima (n = 2), utilizzo di ausili (n = 6). Conclusioni. I dati ottenuti sono molto simili a quelli di un analogo studio effettuato nel 2014. Essi confermano la perdurante difficoltà di gestione dei DMS da parte del Medico Competente aziendale, soprattutto per lavoratori manuali, a maggior impegno biomeccanico. La valutazione di secondo livello (ex art. 39), con la collaborazione di un fisiatra esperto in ergonomia, si dimostra assai utile per superare tale difficoltà. Con tale approccio, infatti, nella grande maggioranza dei casi è possibile la prosecuzione dell’attività lavorativa senza cambio di mansione, nel rispetto di limitazioni e prescrizioni, formulate dopo accurata valutazione funzionale interdisciplinare