Biering-Sorensen test performance of Japanese young males: comparison with other ethnicities and relationship to electromyography, near-infrared spectroscopy and exertion ratings

Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning

Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-source datasets of lower limb EMG signals, especially recording data of Asian race features, are scarce. Additionally, deep learning algorithms are rarely used for human motion intention recognition based on EMG, especially in the lower limb area. In response to these gaps, we present an open-source benchmark dataset of lower limb EMG with Asian race characteristics and large data volume, the JJ dataset, which includes approximately 13,350 clean EMG segments of 10 gait phases from 15 people. This is the first dataset of its kind to include the nine main muscles of human gait when walking. We used the processed time-domain signal as input and adjusted ResNet-18 as the classification tool. Our research explores and compares multiple key issues in this area, including the comparison of sliding time window method and other preprocessing methods, comparison of time-domain and frequency-domain signal processing effects, cross-subject motion recognition accuracy, and the possibility of using thigh and calf muscles in amputees. Our experiments demonstrate that the adjusted ResNet can achieve significant classification accuracy, with an average accuracy rate of 95.34% for human gait phases. Our research provides a valuable resource for future studies in this area and demonstrates the potential for ResNet as a robust and effective method for lower limb human motion intention pattern recognition.

Trunk muscle endurance, strength and flexibility in rural subsistence farmers and urban industrialized adults in western Kenya

OBJECTIVES

High trunk muscle endurance, strength, and moderate flexibility reportedly help maintain musculoskeletal health, but there is evidence for tradeoffs among these variables as well as sex differences in trunk muscle endurance and strength. To test if these observations extend similarly to both men and women in nonindustrial and industrial environments, we investigated intra-individual associations and group and sex differences in trunk muscle endurance, strength, and flexibility among 74 (35 F, 39 M; age range: 18-61 years) adults from the same Kalenjin-speaking population in western Kenya. We specifically compared men and women from an urban community with professions that do not involve manual labor with rural subsistence farmers, including women who frequently carry heavy loads.

METHODS

Trunk muscle endurance, strength, and flexibility were measured with exercise tests and electromyography (EMG).

RESULTS

We found a positive correlation between trunk extensor strength and endurance (R = .271, p ≤ .05) and no associations between strength or endurance and flexibility. Rural women had higher trunk extensor and flexor endurance, EMG-determined longissimus lumborum endurance, and trunk extensor strength than urban women (all p ≤ .05). Rural women had higher trunk extensor and flexor endurance than rural men (both p ≤ .05). Urban women had lower trunk flexor and extensor endurance than urban men (both p ≤ .01).

CONCLUSIONS

High levels of physical activity among nonindustrial subsistence farmers, particularly head carrying among women, appear to be associated with high trunk muscle endurance and strength, which may have important benefits for helping maintain musculoskeletal health.

Validity and everyday clinical applicability of lumbar muscle fatigue assessment methods in patients with chronic non-specific low back pain: a systematic review

PURPOSE

This systematic literature review aimed at examining the validity and applicability in everyday clinical rehabilitation practise of methods for the assessment of back muscle fatiguability in patients with chronic non-specific low back pain (CNSLBP).

METHODS

Extensive research was performed in MEDLINE, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase, Physiotherapy Evidence Database (PEDro) and Cochrane Central Register of Controlled Trials (CENTRAL) databases from their inception to September 2014. Potentially relevant articles were also manually looked for in the reference lists of the identified publications. Studies examining lumbar muscle fatigue in people with CNSLBP were selected. Two reviewers independently selected the articles, carried out the study quality assessment and extracted the results. A modified Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) scale was used to evaluate the scientific rigour of the selected works.

RESULTS

Twenty-four studies fulfilled the selection criteria and were included in the systematic review. We found conflicting data regarding the validity of methods used to examine back muscle fatigue. The Biering-Sorensen test, performed in conjunction with surface electromyography spectral analysis, turned out to be the most widely used and comparatively, the most optimal modality currently available to assess objective back muscle fatigue in daily clinical practise, even though critical limitations are discussed.

CONCLUSIONS

Future research should address the identification of an advanced method for lower back fatigue assessment in patients with CNSLBP which, eventually, might provide physical therapists with an objective and reliable test usable in everyday clinical practise. Implications for Rehabilitation Despite its limitations, the Biering-Sorensen test is currently the most used, convenient and easily available fatiguing test for lumbar muscles. To increase validity and reliability of the Biering-Sorensen test, concomitant activation of synergistic muscles should be taken into account. Pooled mean frequency and half-width of the spectrum are currently the most valid electromyographic parameters to assess fatigue in chronic non-specific low back pain. Body mass index, grading of pain and level of disability of the study population should be reported to enhance research quality.