Developing the "Healthcare CEO App" for patients with type 1 diabetes transitioning from adolescence to young adulthood: A mixed-methods study.

AIM: To develop and test a mobile application that supports the disease self-management of adolescents with type 1 diabetes during their transition to early adulthood. DESIGN: A sequential mixed-methods design was employed. METHODS: The application content was designed according to previously identified care needs and expectations, followed by application development on the Android operating system. From the outpatient clinic of the Department of Paediatric Endocrinology and Metabolism at a medical centre in northern Taiwan, 35 individuals aged between 16-25 years participated in application testing. RESULTS: The overall median score of the QUIS was 4-5, most of the 25% quartile was 4-5, and all of the 75% quartile was 5, indicating adequate user interaction satisfaction.

Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device.

Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients' hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions.

Computer-Aided Detection of Fiducial Points in Seismocardiography through Dynamic Time Warping.

Accelerometer-based devices have been employed in seismocardiography fiducial point detection with the aid of quasi-synchronous alignment between echocardiography images and seismocardiogram signals. However, signal misalignments have been observed, due to the heartbeat cycle length variation. This paper not only analyzes the misalignments and detection errors but also proposes to mitigate the issues by introducing reference signals and adynamic time warping (DTW) algorithm. Two diagnostic parameters, the ratio of pre-ejection period to left ventricular ejection time (PEP/LVET) and the Tei index, were examined with two statistical verification approaches: (1) the coefficient of determination (R2) of the parameters versus the left ventricular ejection fraction (LVEF) assessments, and (2) the receiver operating characteristic (ROC) classification to distinguish the heart failure patients with reduced ejection fraction (HFrEF). Favorable R2 values were obtained, R2 = 0.768 for PEP/LVET versus LVEF and R2 = 0.86 for Tei index versus LVEF. The areas under the ROC curve indicate the parameters that are good predictors to identify HFrEF patients, with an accuracy of more than 92%. The proof-of-concept experiments exhibited the effectiveness of the DTW-based quasi-synchronous alignment in seismocardiography fiducial point detection. The proposed approach may enable the standardization of the fiducial point detection and the signal template generation. Meanwhile, the program-generated annotation data may serve as the labeled training set for the supervised machine learning.

A chemical probe inhibitor targeting STAT1 restricts cancer stem cell traits and angiogenesis in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a worldwide cancer with rising annual incidence. New medications for patients with CRC are still needed. Recently, fluorescent chemical probes have been developed for cancer imaging and therapy. Signal transducer and activator of transcription 1 (STAT1) has complex functions in tumorigenesis and its role in CRC still needs further investigation. METHODS: RNA sequencing datasets in the NCBI GEO repository were analyzed to investigate the expression of STAT1 in patients with CRC. Xenograft mouse models, tail vein injection mouse models, and azoxymethane/dextran sodium sulfate (AOM/DSS) mouse models were generated to study the roles of STAT1 in CRC. A ligand-based high-throughput virtual screening approach combined with SWEETLEAD chemical database analysis was used to discover new STAT1 inhibitors. A newly designed and synthesized fluorescently labeled 4',5,7-trihydroxyisoflavone (THIF) probe (BODIPY-THIF) elucidated the mechanistic actions of STAT1 and THIF in vitro and in vivo. Colonosphere formation assay and chick chorioallantoic membrane assay were used to evaluate stemness and angiogenesis, respectively. RESULTS: Upregulation of STAT1 was observed in patients with CRC and in mouse models of AOM/DSS-induced CRC and metastatic CRC. Knockout of STAT1 in CRC cells reduced tumor growth in vivo. We then combined a high-throughput virtual screening approach and analysis of the SWEETLEAD chemical database and found that THIF, a flavonoid abundant in soybeans, was a novel STAT1 inhibitor. THIF inhibited STAT1 phosphorylation and might bind to the STAT1 SH2 domain, leading to blockade of STAT1-STAT1 dimerization. The results of in vitro and in vivo binding studies of THIF and STAT1 were validated. The pharmacological treatment with BODIPY-THIF or ablation of STAT1 via a CRISPR/Cas9-based strategy abolished stemness and angiogenesis in CRC. Oral administration of BODIPY-THIF attenuated colitis symptoms and tumor growth in the mouse model of AOM/DSS-induced CRC. CONCLUSIONS: This study demonstrates that STAT1 plays an oncogenic role in CRC. BODIPY-THIF is a new chemical probe inhibitor of STAT1 that reduces stemness and angiogenesis in CRC. BODIPY-THIF can be a potential tool for CRC therapy as well as cancer cell imaging.

Cannabinoids orchestrate cross-talk between cancer cells and endothelial cells in colorectal cancer.

Medical marijuana has been approved by the FDA for treating chemotherapy-induced nausea and vomiting. However, less is known about its direct effects on tumor cells and the tumor microenvironment. In this study, RNA-sequencing datasets in the NCBI GEO repository were first analyzed; upregulation of cannabinoid receptors was observed in both primary and metastatic colorectal cancer (CRC) tumor tissues. An increase of cannabinoid receptors was also found in patients with CRC, azoxymethane/dextran sulfate sodium-induced CRC and CRC metastatic mouse models. Δ9-Tetrahydrocannabinol (Δ9-THC)-induced tumor progression in both primary and metastatic mouse models and also increased angiogenesis. A human growth factor antibody array indicated that Δ9-THC promoted the secretion of angiogenic growth factors in CRC, leading to the induction of tube formation and migration in human-induced pluripotent stem cell-derived vascular endothelial cells. The nuclear translocation of STAT1 played important roles in Δ9-THC-induced angiogenesis and tumor progression. Pharmacological treatment with STAT1 antagonist or abrogation of STAT1 with CRISPR/Cas9-based strategy rescued those effects of Δ9-THC in CRC. This study demonstrates that marijuana might increase the risk of CRC progression and that inhibition of STAT1 is a potential strategy for attenuating these side effects.

Design and Implementation of a Wearable Accelerometer-Based Motion/Tilt Sensing Internet of Things Module and Its Application to Bed Fall Prevention.

Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.

Effects of Thermal Annealing on the Properties of Zirconium-Doped MgxZn1-XO Films Obtained through Radio-Frequency Magnetron Sputtering.

Zirconium-doped MgxZn1-xO (Zr-doped MZO) mixed-oxide films were investigated, and the temperature sensitivity of their electric and optical properties was characterized. Zr-doped MZO films were deposited through radio-frequency magnetron sputtering using a 4-inch ZnO/MgO/ZrO2 (75/20/5 wt%) target. Hall measurement, X-ray diffraction (XRD), transmittance, and X-ray photoelectron spectroscopy (XPS) data were obtained. The lowest sheet resistance, highest mobility, and highest concentration were 1.30 × 103 Ω/sq, 4.46 cm2/Vs, and 7.28 × 1019 cm-3, respectively. The XRD spectra of the as-grown and annealed Zr-doped MZO films contained MgxZn1-xO(002) and ZrO2(200) coupled with Mg(OH)2(101) at 34.49°, 34.88°, and 38.017°, respectively. The intensity of the XRD peak near 34.88° decreased with temperature because the films that segregated Zr4+ from ZrO2(200) increased. The absorption edges of the films were at approximately 348 nm under 80% transmittance because of the Mg content. XPS revealed that the amount of Zr4+ increased with the annealing temperature. Zr is a potentially promising double donor, providing up to two extra free electrons per ion when used in place of Zn2+.

Ratio-and-Scale-Aware YOLO for Pedestrian Detection.

Current deep learning methods seldom consider the effects of small pedestrian ratios and considerable differences in the aspect ratio of input images, which results in low pedestrian detection performance. This study proposes the ratio-and-scale-aware YOLO (RSA-YOLO) method to solve the aforementioned problems. The following procedure is adopted in this method. First, ratio-aware mechanisms are introduced to dynamically adjust the input layer length and width hyperparameters of YOLOv3, thereby solving the problem of considerable differences in the aspect ratio. Second, intelligent splits are used to automatically and appropriately divide the original images into two local images. Ratio-aware YOLO (RA-YOLO) is iteratively performed on the two local images. Because the original and local images produce low- and high-resolution pedestrian detection information after RA-YOLO, respectively, this study proposes new scale-aware mechanisms in which multiresolution fusion is used to solve the problem of misdetection of remarkably small pedestrians in images. The experimental results indicate that the proposed method produces favorable results for images with extremely small objects and those with considerable differences in the aspect ratio. Compared with the original YOLOs (i.e., YOLOv2 and YOLOv3) and several state-of-the-art approaches, the proposed method demonstrated a superior performance for the VOC 2012 comp4, INRIA, and ETH databases in terms of the average precision, intersection over union, and lowest log-average miss rate.

Using Digital Image Correlation on SEM Images of Strain Field after Ion Beam Milling for the Residual Stress Measurement of Thin Films.

The residual stress of thin films during the deposition process can cause the components to have unpredictable deformation and damage, which could affect the service life and reliability of the microsystems. Developing an accurate and reliable method for measuring the residual stress of thin films at the micrometer and nanometer scale is a great challenge. To analyze the residual stress regarding factors such as the mechanical anisotropy and preferred orientation of the materials, information related to the in-depth lattice strain function is required when calculating the depth profiles of the residual strain. For depth-resolved measurements of residual stress, it is strategically advantageous to develop a measurement procedure that is microstructurally independent. Here, by performing an incremental focused ion beam (FIB) ring-core drilling experiment with various depth steps, the digital image correlation (DIC) of the specimen images was obtained. The feasibility of DIC to FIB images was evaluated after the translation test, and an appropriate procedure for reliable results was established. Furthermore, the condition of the film in the function of residual stress was assessed and compared to elucidate the applicability of this technology.

Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults.

With advances in technology, wireless and sensor technologies represent a method for continuously recording people's biomedical signals, which may enhance the diagnosis and treatment of users' everyday health conditions. These technologies mostly target older adults. In this study, we examine a smart clothing system targeting clinically high-risk patients, including older adults with cardiovascular disease (31 outpatients) and older adults in general (81 participants), to obtain an understanding of the patients' perception of using wearable healthcare technologies. Given that technology anxiety has been shown to affect users' resistance to using new technology and that perceived ubiquity is considered a characteristic of wearable devices and other mobile wireless technologies, we included three external variables: i.e., technology anxiety, perceived ubiquity, and resistance to change, in addition to the traditional components of the technology acceptance model (TAM). The results of the hypothesized model showed that among older adults in general, technology anxiety had a negative effect on the perceived ease of use and perceived ubiquity. The perceived ubiquity construct affects both user groups' perceived ease of use and perceived usefulness of wearing smart clothes. Most relationships among the original constructs of the TAM were validated in older adults in general. Interestingly, we found that perceived usefulness had an indirect effect on behavioral intention through attitude. These results further confirm the validity of the extended TAM in determining older users' technology acceptance behavior.

Prediction of 30-Day Readmission for COPD Patients Using Accelerometer-Based Activity Monitoring.

Chronic obstructive pulmonary disease (COPD) claimed 3.0 million lives in 2016 and ranked 3rd among the top 10 global causes of death. Moreover, once diagnosed and discharged from the hospital, the 30-day readmission risk in COPD patients is found to be the highest among all chronic diseases. The existing diagnosis methods, such as Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019, Body-mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index, modified Medical Research Council (mMRC), COPD assessment test (CAT), 6-minute walking distance, which are adopted currently by physicians cannot predict the potential readmission of COPD patients, especially within the 30 days after discharge from the hospital. In this paper, a statistical model was proposed to predict the readmission risk of COPD patients within 30-days by monitoring their physical activity (PA) in daily living with accelerometer-based wrist-worn wearable devices. This proposed model was based on our previously reported PA models for activity index (AI) and regularity index (RI) and it introduced a new parameter, quality of activity (QoA), which incorporates previously proposed parameters, such as AI and RI, with other activity-based indices to predict the readmission risk. Data were collected from continuous PA monitoring of 16 COPD patients after hospital discharge as test subjects and readmission prediction criteria were proposed, with a 63% sensitivity and a 37.78% positive prediction rate. Compared to other clinical assessment, diagnosis, and prevention methods, the proposed model showed significant improvement in predicting the 30-day readmission risk.

Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device.

This study condenses huge amount of raw data measured from a MEMS accelerometer-based, wrist-worn device on different levels of physical activities (PAs) for subjects wearing the device 24 h a day continuously. In this study, we have employed the device to build up assessment models for quantifying activities, to develop an algorithm for sleep duration detection and to assess the regularity of activity of daily living (ADL) quantitatively. A new parameter, the activity index (AI), has been proposed to represent the quantity of activities and can be used to categorize different PAs into 5 levels, namely, rest/sleep, sedentary, light, moderate, and vigorous activity states. Another new parameter, the regularity index (RI), was calculated to represent the degree of regularity for ADL. The methods proposed in this study have been used to monitor a subject's daily PA status and to access sleep quality, along with the quantitative assessment of the regularity of activity of daily living (ADL) with the 24-h continuously recorded data over several months to develop activity-based evaluation models for different medical-care applications. This work provides simple models for activity monitoring based on the accelerometer-based, wrist-worn device without trying to identify the details of types of activity and that are suitable for further applications combined with cloud computing services.

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG.

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

Identification of Location Specific Feature Points in a Cardiac Cycle Using a Novel Seismocardiogram Spectrum System.

Seismocardiogram (SCG) or mechanocardiography is a noninvasive cardiac diagnostic method; however, previous studies used only a single sensor to detect cardiac mechanical activities that will not be able to identify location-specific feature points in a cardiac cycle corresponding to the four valvular auscultation locations. In this study, a multichannel SCG spectrum measurement system was proposed and examined for cardiac activity monitoring to overcome problems like, position dependency, time delay, and signal attenuation, occurring in traditional single-channel SCG systems. ECG and multichannel SCG signals were simultaneously recorded in 25 healthy subjects. Cardiac echocardiography was conducted at the same time. SCG traces were analyzed and compared with echocardiographic images for feature point identification. Fifteen feature points were identified in the corresponding SCG traces. Among them, six feature points, including left ventricular lateral wall contraction peak velocity, septal wall contraction peak velocity, transaortic peak flow, transpulmonary peak flow, transmitral ventricular relaxation flow, and transmitral atrial contraction flow were identified. These new feature points were not observed in previous studies because the single-channel SCG could not detect the location-specific signals from other locations due to time delay and signal attenuation. As the results, the multichannel SCG spectrum measurement system can record the corresponding cardiac mechanical activities with location-specific SCG signals and six new feature points were identified with the system. This new modality may help clinical diagnoses of valvular heart diseases and heart failure in the future.

Realization of a CORDIC-Based Plug-In Accelerometer Module for PSG System in Head Position Monitoring for OSAS Patients.

Overnight polysomnography (PSG) is currently the standard diagnostic procedure for obstructive sleep apnea (OSA). It has been known that monitoring of head position in sleep is crucial not only for the diagnosis (positional sleep apnea) but also for the management of OSA (positional therapy). However, there are no sensor systems available clinically to hook up with PSG for accurate head position monitoring. In this paper, an accelerometer-based sensing system for accurate head position monitoring is developed and realized. The core CORDIC- (COordinate Rotation DIgital Computer-) based tilting sensing algorithm is realized in the system to quickly and accurately convert accelerometer raw data into the desired head position tilting angles. The system can hook up with PSG devices for diagnosis to have head position information integrated with other PSG-monitored signals. It has been applied in an IRB test in Taipei Veterans General Hospital and has been proved that it can meet the medical needs of accurate head position monitoring for PSG diagnosis.

Reliability of mechanomyographic amplitude measurements for trunk muscles during maximal voluntary isometric contraction.

BACKGROUND: Mechanomyography (MMG) has been used to investigate mechanical characteristics of muscle contraction in clinical and experimental settings. OBJECTIVE: The aim of this study was to determine the test-retest reliability of mechanomyographic amplitude (MMGRMS) measurements as a tool for measuring the maximal voluntary isometric contractions (MVICs) of trunk muscles in healthy participants. METHODS: There were ten young adults participating in this study. Accelerometers were used to detect surface MMG signals from three trials of 5-s MVICs of the rectus abdominis, external obliques, erector spinae, and multifidus in the vertical, transverse, and longitudinal directions. Intraclass correlation coefficient (ICC), standard error of measurement (SEM), and minimum detectable change were calculated. RESULTS: Good to excellent test-retest reliability of mechanomyographic amplitude (MMGRMS) measurements was achieved for all MVICs of trunk muscles in healthy participants, as indicated by ICCs ranging from 0.99 to 0.64 for MMGRMS of the trunk muscles during MVIC. CONCLUSIONS: This study demonstrates that MMG is a reliable measurement to detect the activation amplitudes of trunk muscles during MVIC.

Development of a Wearable Instrumented Vest for Posture Monitoring and System Usability Verification Based on the Technology Acceptance Model.

Body posture and activity are important indices for assessing health and quality of life, especially for elderly people. Therefore, an easily wearable device or instrumented garment would be valuable for monitoring elderly people's postures and activities to facilitate healthy aging. In particular, such devices should be accepted by elderly people so that they are willing to wear it all the time. This paper presents the design and development of a novel, textile-based, intelligent wearable vest for real-time posture monitoring and emergency warnings. The vest provides a highly portable and low-cost solution that can be used both indoors and outdoors in order to provide long-term care at home, including health promotion, healthy aging assessments, and health abnormality alerts. The usability of the system was verified using a technology acceptance model-based study of 50 elderly people. The results indicated that although elderly people are anxious about some newly developed wearable technologies, they look forward to wearing this instrumented posture-monitoring vest in the future.

A quantitative method for evaluating inferior glenohumeral joint stiffness using ultrasonography.

Subluxation of the affected shoulder in post-stroke patients is associated with nerve disorders and muscle fatigue. Clinicians must be able to accurately and reliably measure inferior glenohumeral subluxation in patients to provide appropriate treatment. However, quantitative methods for evaluating the laxity and stiffness of the glenohumeral joint (GHJ) are still being developed. The aim of this study was to develop a new protocol for evaluating the laxity and stiffness of the inferior GHJ using ultrasonography under optimal testing conditions and to investigate changes in the GHJ from a commercially available humerus brace and shoulder brace. Multistage inferior displacement forces were applied to create a glide between the most cephalad point on the visible anterosuperior surface of the humeral head and coracoid process in seven healthy volunteers. GHJ stiffness was defined as the slope of the linear regression line between the glides and different testing loads. The testing conditions were defined by different test loading mechanisms (n=2), shoulder constraining conditions (n=2), and loading modes (n=4). The optimal testing condition was defined as the condition with the least residual variance of measured laxity to the calculated stiffness under different testing loads. A paired t-test was used to compare the laxity and stiffness of the inferior GHJ using different braces. No significant difference was identified between the two test loading mechanisms (t=0.218, p=0.831) and two shoulder constraining conditions (t=-0.235, p=0.818). We concluded that ultrasonographic laxity measurements performed using a pulley set loading mechanism was as reliable as direct loading. Additionally, constraining the unloaded shoulder was proposed due to the lower mean residual variance value. Moreover, pulling the elbow downward with loading on the upper arm was suggested, as pulling the elbow downward with the elbow flexed and loading on the forearm may overestimate stiffness and pain in the inferior GHJ at the loading point due to friction between the wide belt and skin. Furthermore, subjects wearing a humerus brace with a belt, which creates the effect of lifting the humerus toward the acromion, had greater GHJ stiffness compared to subjects wearing a shoulder brace without a belt to lift the humerus under the proposed testing conditions. This study provides experimental evidence that shoulder braces may reduce GHJ laxity under an external load, implying that the use of a humeral brace can prevent subluxation in post-stroke patients. The resulting optimal testing conditions for measuring the laxity and stiffness of the GHJ is to constrain the unloaded shoulder and bend the loaded arm at the elbow with loading on the upper arm using a pulley system.