Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications.

Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs. Remote monitoring and automated classification are expected to revolutionize wound care by allowing patients to self-monitor their wound pathology, assist in the remote triaging of patients by clinicians, and allow for more immediate interventions when necessary. This scoping review provides an overview of applicable CV and ML techniques. This includes automated CV methods developed for remote assessment of wound photographs, as well as predictive ML algorithms that leverage heterogeneous data streams. We discuss the benefits of such applications and the role they may play in diabetic foot care moving forward. We highlight both the need for, and possibilities of, computational sensing systems to improve diabetic foot care and bring greater knowledge to patients in need.

Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

BACKGROUND: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device to measure speech that participants encounter as an indicator of social interaction. METHODS: Twenty nine participants with LLD and 29 age-matched controls wore a wrist-worn device continuously for seven days, which recorded their acoustic environment. Acoustic data were automatically analysed using deep learning models that had been developed and validated on an independent speech dataset. Total speech activity and the proportion of speech produced by the device wearer were both detected whilst maintaining participants' privacy. Participants underwent a neuropsychological test battery and clinical and self-report scales to measure severity of depression, general and social functioning. RESULTS: Compared to controls, participants with LLD showed poorer self-reported social and general functioning. Total speech activity was much lower for participants with LLD than controls, with no overlap between groups. The proportion of speech produced by the participants was smaller for LLD than controls. In LLD, both speech measures correlated with attention and psychomotor speed performance but not with depression severity or self-reported social functioning. CONCLUSIONS: Using this device, LLD was associated with lower levels of speech than controls and speech activity was related to psychomotor retardation. We have demonstrated that speech activity measured by wearable technology differentiated LLD from controls with high precision and, in this study, provided an objective measure of an aspect of real-world social functioning in LLD.

A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.

BACKGROUND: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. OBJECTIVE: The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. METHODS: The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. RESULTS: Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered "unhealthy" and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. CONCLUSIONS: The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.

Prompting arm activity after stroke: A clinical proof of concept study of wrist-worn accelerometers with a vibrating alert function.

BACKGROUND: Frequent practice of functional movements after stroke may optimise motor recovery; however, it is challenging for patients to remember to integrate an impaired limb into daily activities. We report the activity responses of stroke patients receiving a vibrating alert delivered by a tri-axial accelerometer wristband to prompt movement of the impaired arm if hourly activity levels fell. METHODS: Adults with upper limb impairment ≤28 days post-stroke wore the device for four weeks. Therapists and patients reviewed movement activity data twice weekly to agree ongoing rehabilitation activities and programme the wristband with a personalised prompt threshold (median baseline activity + 5%, 25% or 50%).Results: Seven patients completed the programme (five males; mean ± standard deviation (age) 64 ± 5 years; days post-stroke 13 ± 7; baseline/four-week Action Research Arm Test median (Interquartile range (IQR)) 39 (8, 44)/56 (11, 57)). Wristbands were worn for 89% of programme duration. A total of 1,288 prompts were delivered, with a median of four (IQR 3,7) prompts per patient per day. Mean activity increases following a prompt ranged from 11% to 29%. CONCLUSIONS: Feedback delivered by a programmable accelerometer increased impaired arm activity. Improvements are required in device reliability before conducting a pragmatic clinical trial to examine the impact upon recovery.

Associations between prolonged sedentary time and breaks in sedentary time with cardiometabolic risk in 10-14-year-old children: The HAPPY study.

This study examines the association between prolonged sedentary time and breaks in sedentary time with cardiometabolic risk in 10-14-year-old children. This cross-sectional design study analysed accelerometry-determined sedentary behaviour and physical activity collected over 7 days from 111 (66 girls) UK schoolchildren. Objective outcome measures included waist circumference, fasting lipids, fasting glucose, blood pressure, and cardiorespiratory fitness. Logistic regression was used for the main data analysis. After adjustment for confounders, the odds of having hypertriglyceridaemia (P = 0.03) and an increased clustered cardiometabolic risk score (P = 0.05) were significantly higher in children who engaged in more prolonged sedentary bouts per day. The number of breaks in sedentary time per day was not associated with any cardiometabolic risk factor, but longer mean duration of daily breaks in sedentary time were associated with a lower odds of having abdominal adiposity (P = 0.04) and elevated diastolic blood pressure (P = 0.01). These associations may be mediated by engagement in light activity. This study provides evidence that avoiding periods of prolonged uninterrupted sedentary time may be important for reducing cardiometabolic disease risk in children.

A study of physical activity comparing people with Charcot-Marie-Tooth disease to normal control subjects.

PURPOSE: Charcot Marie Tooth disease (CMT) describes a group of hereditary neuropathies that present with distal weakness, wasting and sensory loss. Small studies indicate that people with CMT have reduced daily activity levels. This raises concerns as physical inactivity increases the risk of a range of co- morbidities, an important consideration in the long-term management of this disease. This study aimed to compare physical activity, patterns of sedentary behavior and overall energy expenditure of people with CMT and healthy matched controls. METHODS: We compared 20 people with CMT and 20 matched controls in a comparison of physical activity measurement over seven days, using an activity monitor. Patterns of sedentary behavior were explored through a power law analysis. RESULTS: Results showed a decrease in daily steps taken in the CMT group, but somewhat paradoxically, they demonstrate shorter bouts of sedentary activity and more frequent transitions from sedentary to active behaviors. No differences were seen in energy expenditure or time spent in sedentary, moderate or vigorous activity. CONCLUSION: The discrepancy between energy expenditure and number of steps could be due to higher energy requirements for walking, but also may be due to an over-estimation of energy expenditure by the activity monitor in the presence of muscle wasting. Alternatively, this finding may indicate that people with CMT engage more in activities or movement not related to walking. Implications for Rehabilitation Charcot-Marie-Tooth disease: • People with Charcot-Marie-Tooth disease did not show a difference in energy expenditure over seven days compared to healthy controls, but this may be due to higher energy costs of walking, and/or an over estimation of energy expenditure by the activity monitor in a population where there is muscle wasting. This needs to be considered when interpreting activity monitor data in people with neuromuscular diseases. • Compared to healthy controls, people with Charcot-Marie-Tooth disease had a lower step count over seven days, but exhibited more frequent transitions from sedentary to active behaviors • High Body Mass Index and increased time spent sedentary were related factors that have implications for general health status. • Understanding the profile of physical activity and behavior can allow targeting of rehabilitation interventions to address mobility and fitness.

Wristband Accelerometers to motiVate arm Exercise after Stroke (WAVES): study protocol for a pilot randomized controlled trial.

BACKGROUND: Loss of upper limb function affects up to 85 % of acute stroke patients. Recovery of upper limb function requires regular intensive practise of specific upper limb tasks. To enhance intensity of practice interventions are being developed to encourage patients to undertake self-directed exercise practice. Most interventions do not translate well into everyday activities and stroke patients continue to find it difficult remembering integration of upper limb movements into daily activities. A wrist-worn device has been developed that monitors and provides 'live' upper limb activity feedback to remind patients to use their stroke arm in daily activities (The CueS wristband). The aim of this trial is to assess the feasibility of a multi-centre, observer blind, pilot randomised controlled trial of the CueS wristband in clinical stroke services. METHODS/DESIGN: This pilot randomised controlled feasibility trial aims to recruit 60 participants over 15 months from North East England. Participants will be within 3 months of stroke which has caused new reduced upper limb function and will still be receiving therapy. Each participant will be randomised to an intervention or control group. Intervention participants will wear a CueS wristband (between 8 am and 8 pm) providing "live" feedback towards pre-set movement goals through a simple visual display and vibration prompts whilst undertaking a 4-week upper limb therapy programme (reviewed twice weekly by an occupational/physiotherapist). Control participants will also complete the 4-week upper limb therapy programme but will wear a 'sham' CueS wristband that monitors upper limb activity but provides no feedback. Outcomes will determine study feasibility in terms of recruitment, retention, adverse events, adherence and collection of descriptive clinical and accelerometer motor performance data at baseline, 4 weeks and 8 weeks. DISCUSSION: The WAVES study will address an important gap in the evidence base by reporting the feasibility of undertaking an evaluation of emerging and affordable technology to encourage impaired upper limb activity after stroke. The study will establish whether the study protocol can be supported by clinical stroke services, thereby informing the design of a future multi-centre randomised controlled trial of clinical and cost-effectiveness. TRIAL REGISTRATION: ISRCTN:82306027 . Registered 12 July 2016.

Unsupervised home monitoring of Parkinson's disease motor symptoms using body-worn accelerometers.

INTRODUCTION: Current PD assessment methods have inherent limitations. There is need for an objective method to assist clinical decisions and to facilitate evaluation of treatments. Accelerometers, and analysis using artificial neural networks (ANN), have shown potential as a method of motor symptom evaluation. This work describes the development of a novel PD disease state detection system informed by algorithms based on data collected in an unsupervised, home environment. We evaluated whether this approach can reproduce patient-completed symptom diaries and clinical assessment of disease state. METHODS: 34 participants with PD wore bilateral wrist-worn accelerometers for 4 h in a research facility (phase 1) and for 7 days at home whilst completing symptom diaries (phase 2). An ANN to predict disease state was developed based on home-derived accelerometer data. Using a leave-one-out approach, ANN performance was evaluated against patient-completed symptom diaries and against clinician rating of disease state. RESULTS: In the clinical setting, specificity for dyskinesia detection was extremely high (0.99); high specificity was also demonstrated for home-derived data (0.93), but with low sensitivity (0.38). In both settings, sensitivity for on/off detection was sub-optimal. ANN-derived values of the proportions of time in each disease state showed strong, significant correlations with patient-completed symptom diaries. CONCLUSION: Accurate, real-time evaluation of symptoms in an unsupervised, home environment, with this sensor system, is not yet achievable. In terms of the amounts of time spent in each disease state, ANN-derived results were comparable to those of symptom diaries, suggesting this method may provide a valuable outcome measure for medication trials.

Automated video-based assessment of surgical skills for training and evaluation in medical schools.

PURPOSE: Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches however are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities. METHOD: We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis. RESULTS: We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos. CONCLUSION: Our evaluations show that frequency features perform better than motion texture features, which in-turn perform better than symbol-/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.

Non-alcoholic fatty liver disease is associated with higher levels of objectively measured sedentary behaviour and lower levels of physical activity than matched healthy controls.

BACKGROUND AND AIMS: Physical activity is a key determinant of metabolic control and is recommended for people with non-alcoholic fatty liver disease (NAFLD), usually alongside weight loss and dietary change. To date, no studies have reported the relationship between objectively measured sedentary behaviour and physical activity, liver fat and metabolic control in people with NAFLD, limiting the potential to target sedentary behaviour in clinical practice. This study determined the level of sedentary behaviour and physical activity in people with NAFLD, and investigated links between physical activity, liver fat and glucose control. METHODS: Sedentary behaviour, physical activity and energy expenditure were assessed in 37 adults with NAFLD using a validated multisensor array over 7 days. Liver fat and glucose control were assessed, respectively, by 1H-MRS and fasting blood samples. Patterns of sedentary behaviour were assessed by power law analyses of the lengths of sedentary bouts fitted from raw sedentary data. An age and sex-matched healthy control group wore the activity monitor for the same time period. RESULTS: People with NAFLD spent approximately half an hour extra a day being sedentary (1318±68 vs1289±60 mins/day; p<0.05) and walked 18% fewer steps (8483±2926 vs 10377±3529 steps/day; p<0.01). As a consequence, active energy expenditure was reduced by 40% (432±258 vs 732±345 kcal/day; p<0.01) and total energy expenditure was lower in NAFLD (2690±440 vs 2901±511 kcal/day; p<0.01). Power law analyses of the lengths of sedentary bouts demonstrated that patients with NAFLD also have a lower number of transitions from being sedentary to active compared with controls (13±0.03 vs15±0.03%; p<0.05). CONCLUSIONS: People with NAFLD spend more time sedentary and undertake less physical activity on a daily basis than healthy controls. High levels of sedentary behaviour and low levels of physical activity represent a therapeutic target that may prevent progression of metabolic conditions and weight gain in people with NAFLD and should be considered in clinical care.