It is well known that centre of pressure (CoP) displacement correlates negatively with the maximal isometric torque (MIT) of ankle muscles. This relationship has never been investigated in elderly fallers. The purpose of this study was thus to analyse the relationship between the MIT of ankle muscles and CoP displacement in upright stance in a sample aged between 18 and 90 years old that included elderly fallers. The aim was to identify a threshold of torque below which balance is compromised. The MIT of Plantar flexors (PF) and dorsal flexors (DF) and CoP were measured in 90 volunteers: 21 healthy young adults (age: 24.1 ± 5.0), 12 healthy middle-aged adults (age: 50.2 ± 4.5), 27 healthy elderly non-fallers (age: 75.5 ± 7.0) and 30 elderly fallers (age: 78.8 ± 6.7). The MIT of PF and DF were summed to obtain the overall maximal ankle muscle strength. Body weight and height were used to normalize MIT (nMIT) and CoP (nCoP), respectively. nCoP correlated negatively with nMIT. 90% of elderly fallers generated an nMIT 3.1 N∙m∙kg-1. The relationship between nMIT and nCoP implies that ankle muscle weakness contributes to increased postural instability and the risk of falling. We observed that below the threshold of 3.1 N∙m∙kg-1, postural stability was dramatically diminished and balance was compromised. Our results suggest that measuring ankle torque could be used in routine clinical practice to identify potential fallers.

Objective. To evaluate the effects of loss of range of motion (ROM) of the metatarsophalangeal (MTP) joint on the kinematic parameters of walking in rheumatoid arthritis (RA) patients. Methods. Inclusion of RA patients with inactive disease, no synovitis of the inferior limb and reduced ROM of the MTP joints. Evaluation of the ROM of the MTP dorsal and plantar flexion, and gait analysis using a three-dimensional computerized movement analysis. Calculation of gait parameters and maximal flexion and extension of the hips and knees during walking. Analysis 1 compared the ROM of dorsal and plantar flexion in patients with or without walking pain; 2 compared the gait parameters between patients and controls; 3 investigated a relationship between gait parameters and (i) the ROM of the MTP dorsal and plantar flexion and (ii) the pain at walking; 4 investigated the relationship between the ROM of the MTP dorsal and plantar flexion and maximal flexion and extension of the hip and knee joints during walking. Results. Nine patients and seven controls were included. The MTP ROM was no different in patients presenting with or without pain at walking. The walking velocity was lower and the stride length shorter in patients than in controls. The walking velocity and the stride length were positively related to the MTP dorsal flexion ROM (r²=0.75 and 0.67). There was a negative relationship between maximal flexion of the knee and hips during walking and the underlying MTP dorsal flexion ROM (r²=0.67 and 0.54). Conclusion. In RA patients, reduced MTP dorsal flexion mobility induces changes in the walking parameters, including the kinematics of the overlying lower limb joints. Treatment of an RA-impaired forefoot should focus on MTP mobility as well as on pain.

The main aim of our study was to investigate the possibility of applying machine learning techniques to the analysis of electromyographic patterns (EMG) collected from arthritic patients during gait. The EMG recordings were collected from the lower limbs of patients with arthritis and compared with those of healthy subjects (CO) with no musculoskeletal disorder. The study involved subjects suffering from two forms of arthritis, viz, rheumatoid arthritis (RA) and hip osteoarthritis (OA). The analysis of the data was plagued by two problems which frequently render the analysis of this type of data extremely difficult. One was the small number of human subjects that could be included in the investigation based on the terms specified in the inclusion and exclusion criteria for the study. The other was the high intra- and inter-subject variability present in EMG data. We identified some of the muscles differently employed by the arthritic patients by using machine learning techniques to classify the two groups and then identified the muscles that were critical for the classification. For the classification we employed least-squares kernel (LSK) algorithms, neural network algorithms like the Kohonen self organizing map, learning vector quantification and the multilayer perceptron. Finally we also tested the more classical technique of linear discriminant analysis (LDA). The performance of the different algorithms was compared. The LSK algorithm showed the highest capacity for classification. Our study demonstrates that the newly developed LSK algorithm is adept for the treatment of biological data. The muscles that were most important for distinguishing the RA from the CO subjects were the soleus and biceps femoris. For separating the OA and CO subjects however, it was the gluteus medialis muscle. Our study demonstrates how classification with EMG data can be used in the clinical setting. While such procedures are unnecessary for the diagnosis of the type of arthritis present, an understanding of the muscles which are responsible for the classification can help to better identify targets for rehabilitative measures.