Wearable lower-limb joint angle estimation using a reduced inertial measurement unit (IMU) sensor set could enable quick, economical sports injury risk assessment and motion capture; however the vast ...majority of existing research requires a full IMU set attached to every related body segment and is implemented in only a single movement, typically walking. We thus implemented 3-dimensional knee and hip angle estimation with a reduced IMU sensor set during yoga, golf, swimming (simulated lower body swimming in a seated posture), badminton, and dance movements. Additionally, current deep-learning models undergo an accuracy drop when tested with new and unseen activities, which necessitates collecting large amounts of data for the new activity. However, collecting large datasets for every new activity is time-consuming and expensive. Thus, a transfer learning (TL) approach with long short-term memory neural networks was proposed to enhance the model's generalization ability towards new activities while minimizing the need for a large new-activity dataset. This approach could transfer the generic knowledge acquired from training the model in the source-activity domain to the target-activity domain. The maximum improvement in estimation accuracy (RMSE) achieved by TL is 23.6 degrees for knee flexion/extension and 22.2 degrees for hip flexion/extension compared to without TL. These results extend the application of motion capture with reduced sensor configurations to a broader range of activities relevant to injury prevention and sports training. Moreover, they enhance the capacity of data-driven models in scenarios where acquiring a substantial amount of training data is challenging.
Accurate shoulder joint angle estimation is crucial for analyzing joint kinematics and kinetics across a spectrum of movement applications including in athletic performance evaluation, injury ...prevention, and rehabilitation. However, accurate IMU-based shoulder angle estimation is challenging and the specific influence of key error factors on shoulder angle estimation is unclear. We thus propose an analytical model based on quaternions and rotation vectors that decouples and quantifies the effects of two key error factors, namely sensor-to-segment misalignment and sensor orientation estimation error, on shoulder joint rotation error. To validate this model, we conducted experiments involving twenty-five subjects who performed five activities: yoga, golf, swimming, dance, and badminton. Results showed that improving sensor-to-segment misalignment along the segment's extension/flexion dimension had the most significant impact in reducing the magnitude of shoulder joint rotation error. Specifically, a 1° improvement in thorax and upper arm calibration resulted in a reduction of 0.40° and 0.57° in error magnitude. In comparison, improving IMU heading estimation was only roughly half as effective (0.23° per 1°). This study clarifies the relationship between shoulder angle estimation error and its contributing factors, and identifies effective strategies for improving these error factors. These findings have significant implications for enhancing the accuracy of IMU-based shoulder angle estimation, thereby facilitating advancements in IMU-based upper limb rehabilitation, human-machine interaction, and athletic performance evaluation.
Abstract Background Telerehabilitation is a promising avenue for improving patient outcomes and expanding accessibility. However, there is currently no spine-related assessment for telerehabilitation ...that covers multiple exercises. Methods We propose a wearable system with two inertial measurement units (IMUs) to identify IMU locations and estimate spine angles for ten commonly prescribed spinal degeneration rehabilitation exercises (supine chin tuck head lift rotation, dead bug unilateral isometric hold, pilates saw, catcow full spine, wall angel, quadruped neck flexion/extension, adductor open book, side plank hip dip, bird dog hip spinal flexion, and windmill single leg). Twelve healthy subjects performed these spine-related exercises, and wearable IMU data were collected for spine angle estimation and IMU location identification. Results Results demonstrated average mean absolute spinal angle estimation errors of 2.59 $$^\circ$$ ∘ and average classification accuracy of 92.97%. The proposed system effectively identified IMU locations and assessed spine-related rehabilitation exercises while demonstrating robustness to individual differences and exercise variations. Conclusion This inexpensive, convenient, and user-friendly approach to spine degeneration rehabilitation could potentially be implemented at home or provide remote assessment, offering a promising avenue to enhance patient outcomes and improve accessibility for spine-related rehabilitation. Trial registration: No. E2021013P in Shanghai Jiao Tong University.
Wireless inertial motion capture holds promise for real-time human-machine interfaces and home-based rehabilitation applications. However, wireless data drop can cause significant estimation errors ...deteriorating performance or even making the system unusable. It is currently unclear how to estimate non-periodic kinematics with wearable inertial measurement units (IMUs) in the presence of wireless data drop (packet loss). We thus propose a novel inference encoder-decoder network model for real-time kinematics during dynamic movement. Twenty-four healthy subjects performed yoga, golf, swimming, dance, and badminton movement activities while wearing IMUs and 10-90% of each IMU's data were randomly removed to determine the effects of data drop on estimation accuracy with and without the proposed model. Results demonstrated a reduction in RMSE of 45.2% to 51.5% in the upper limb kinematic estimation of the proposed model compared to the No Prediction strategy, and a reduction of 19.1% to 31.3% of the proposed model compared with an baseline LSTM model. In addition, the proposed model has significantly less error (p<0.05) than the No Prediction strategy and the baseline LSTM model for 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80% data drop. These results could enable wearable, wireless IMU dynamic motion analysis and assessment with reduced kinematic estimation error in the presence of varying amounts of wireless data drop and thus could further facilitate human-machine interaction and home-based medical assessment and treatment.
Simultaneous decoding of hand gestures and force levels has the potential to unlock a broad range of home-based application scenarios in post-stroke rehabilitation. However, current wearable ...approaches cannot struggle to balance wearability and functionality well. We thus propose and investigate the feasibility of wearable photoplethysmography (PPG) for simultaneous hand gesture and force level classification. A customized wristband embedded with green, red, and infrared light PPG sensors was developed. Fourteen subjects wore this wristband and performed four pinching gestures at 10%, 40%, and 70% maximum volunteer contraction (MVC) force levels. Results showed that the proposed method achieved satisfactory recognition accuracy, and the combination of three reflected light PPG signals was more accurate than any single signal or two-signal combinations. Force level classification accuracy within each hand gesture was less robust to the variety of hand gestures, and hand gesture classification accuracy at different force levels decreased as the force level increased. In addition, the proposed method still achieved reasonable recognition accuracy, with a relatively small amount of training data, potentially saving time in practical applications. These results demonstrate the feasibility of a wearable, low-cost PPG approach for simultaneous hand gesture and force level classification, which could facilitate more widespread hand mobility and strength training both inside and outside the clinic.
Estimating hand and body posture is crucial for enabling human-robot collaboration, preventing occupational diseases, and training humanoid robots. Although advances in wearable motion sensors, such ...as Inertial Measurement Units (IMUs), have resulted in public datasets in industrial and occupational settings, these datasets rarely include movements with subjects holding and manipulating objects. However, it is crucial to have data on how humans move and interact with different objects so that we can better understand human motion intention and movement strategies in specific scenarios. We thus propose the HBOD dataset (hand-body-object dataset), which encompasses synchronized human pose data from an IMU sensor network, hand posture data from a smart data glove, and object position and attitude information obtained from a motion capture system, while subjects move and interact with a screwdriver, hammer, spanner, electric drill, and a rectangular workpiece. This paper provides an overview of the hardware setup, experimental protocol, data format, and data visualization results. This dataset provides crucial object information absent from existing datasets, thus offering highly valuable manipulation data for occupational diseases research, human-robot interaction, and robot skill acquisition.
Mesenchymal stem cells (MSC) are not universal and may be subject to dynamic changes upon local milieus in vivo and after isolation and cultivation in vitro. Here, we demonstrate that MSC derived ...from murine pericardial adipose tissue (pMSC) constitute two cohorts of population distinguished by the level of CD73 expression (termed as CD73high and CD73low pMSC). Transplantation of two types of cells into mouse hearts after myocardial infarction (MI) revealed that the CD73high pMSC preferentially brought about structural and functional repair in comparison to the PBS control and CD73low pMSC. Furthermore, the CD73high pMSC displayed a pronounced anti-inflammatory activity by attenuating CCR2+ macrophage infiltration and upregulating several anti-inflammatory genes 5 days after in vivo transplantation and ex vivo cocultivation with peritoneal macrophages. The immunomodulatory effect was not seen in cocultivation experiments with pMSC derived from CD73 knockout mice (CD73-/-) but was partially blocked by pretreatment of the A2b receptor antagonist, PSB603. The results highlight a heterogeneity of the CD73 expression that may be related to its catalytic products on the modulation of the local immune response and thus provide a possible explanation to the inconsistency of the regenerative results when different sources of donor cells were used in stem cell-based therapy.
Neuroblastoma (NB) is a pediatric cancer occurring in the peripheral nervous system. A demethylase, alkylation repair homolog protein 5 (ALKBH5), is one type of N6-methyladenosine (m6A) eraser that ...plays a tumor-suppressive role in a variety of cancers. The significance of carbohydrate metabolism in cancer has been intensively investigated over the years, but the correlation between ALKBH5 and glucose metabolism in NB remains to be elucidated.
Based on the overlapped genes (DE-GRGs) of ALKBH5-related differentially expressed genes (ALKBH5-DEGs) in GSE62564 (n=498) and genes related to glucose metabolism (GRGs), a LASSO regression model was constructed. External validations with datasets (EGAS00001001308, n=139 & GSE16476, n=88) and the NB samples from Shanghai Children's Hospital (SCH) were performed. Meanwhile, biological and clinical utility, immune cell subtypes and drug sensitivity were assessed.
ALKBH5 was significantly correlated with better overall survival (OS) in NB patients, and gene set enrichment analysis (GSEA) showed its enrichment in GO/ KEGG terms regarding glucose metabolism. 27 of the 31 DE-GRGs were included in the LASSO screen after the univariate analysis. A prognostic glucometabolic model including AHCY, NCAN, FBP2, GALNT3 and AKR1C2 was established with the internal and external validation with biological experiments: the high-risk subtype compared to the low-risk subtype showed oncogenic and MYCN-related malignancy, glucometabolic dysregulation, poor prognosis and immunosuppression. TGX-221 was predicted to be a potential therapeutic drug and validated to suppress NB oncogenes including MYCN, AHCY and NCAN and immunosuppressive DNMT1 in NB cells.
ALKBH5 was closely related to glucometabolic processes, and our prognostic model had high application value in predicting & assessing the OS of NB patients, and even served potential drug targets.
Neuroblastoma (NB) is a kind of typical life-threatening extracranial tumor in children. N6-methyladenosine (m6A) modification is closely related to multiple cancer pathological processes. ...Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) is a top-ranked prognostic risk gene in NB; however, its function is uncertain. The expression of m6A-associated enzymes in patients with NB was analyzed using the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The IGF2BP3 level in NB cell lines and primary samples was tested using quantitative real-time polymerase chain reaction (qRT-PCR), western blot method, and immunohistochemical analysis. The IGF2BP3 function in cell proliferation was clarified based on many functional in vitro and in vivo experiments. The interaction between IGF2BP3 and N-myc was researched via RNA immunoprecipitation (RIP), m6A RNA immunoprecipitation (MeRIP), and chromatin immunoprecipitation (ChIP) assays. The 16 m6A-regulated enzymes in NB were researched, and the result indicated that IGF2BP3 overexpression was related to cancer progression, COG risk, and survival based on the GEO and TARGET databases. Besides, the IGF2BP3 and MYCN levels were positively correlated. IGF2BP3 expression levels increased in MYCN-amplified NB clinical samples and cells. Knockdown of IGF2BP3 inhibited N-myc expression and NB cell proliferation in vitro and in vivo. IGF2BP3 regulates MYCN RNA stability by modifying m6A. In addition, we demonstrated that N-myc is a transcription factor that directly promotes IGF2BP3 expression in NB cells. IGF2BP3 regulates the proliferation of NB cells via m6A modification of MYCN. N-myc also acts as a transcription factor that regulates IGF2BP3 expression. A positive feedback loop between IGF2BP3 and N-myc facilitates NB cell proliferation.
Neuroblastoma (NB) is the most common extracranial solid tumor occurring during childhood and high-risk NB patients have a poor prognosis. The amplified MYCN gene serves as an important determinant ...of a high risk of NB.
We performed an integrative screen using public NB tissue and cell line data, and identified that SMAD9 played an important role in high-risk NB. An investigation of the super-enhancers database (SEdb) and chromatin immunoprecipitation sequencing (ChIP-seq) dataset along with biological experiments of incorporating gene knockdown and CRISPR interference (CRISPRi) were performed to identify upstream regulatory mechanism of SMAD9. Gene knockdown and rescue, quantitative real-time PCR (Q-RT-PCR), cell titer Glo assays, colony formation assays, a subcutaneous xenograft model and immunohistochemistry were used to determine the functional role of SMAD9 in NB. An integrative analysis of ChIP-seq data with the validation of CRISPRi and dual-luciferase reporter assays and RNA sequencing (RNA-seq) data with Q-RT-PCR validation was conducted to analyze the downstream regulatory mechanism of SMAD9.
High expression of SMAD9 was specifically induced by the transcription factors including MYCN, PHOX2B, GATA3 and HAND2 at the enhancer region. Genetic suppression of SMAD9 inhibited MYCN-amplified NB cell proliferation and tumorigenicity both in vitro and in vivo. Further studies revealed that SMAD9 bound to the MYCN promoter and transcriptionally regulate MYCN expression, with MYCN reciprocally binding to the SMAD9 enhancer and transactivating SMAD9, thus forming a positive feedback loop along with the MYCN-associated cancer cell cycle.
This study delineates that SMAD9 forms a positive transcriptional feedback loop with MYCN and represents a unique tumor-dependency for MYCN-amplified neuroblastoma.
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