Sunghoon Ivan Lee

Determining the Single Best Axis for Exercise Repetition Recognition and Counting on SmartWatches

Due to the exploding costs of chronic diseasesstemming from physical inactivity, wearable sensor systems toenable remote, continuous monitoring of individuals has increasedin popularity. Many research and commercial systems exist inorder to track the activity levels of users from general dailymotion to detailed movements. This work examines this problemfrom the space of smartwatches, using the Samsung GalaxyGear, a commercial device containing an accelerometer and agyroscope, to be used in recognizing physical activity. This workalso shows the sensors and features necessary to enable suchsmartwatches to accurately count, in real-time, the repetitions offree-weight and body-weight exercises. The goal of this work isto try and select only the best single axis for each activity byextracting only the most informative activity-specific features, inorder to minimize computational load and power consumptionin repetition counting. The five activities are incorporated in aworkout routine, and knowing this information, a random forestclassifier is built with average area under the curve (AUC) of: 974, with average accuracy of 93%, in cross validation to identify eachrepetition of a given exercise using all available sensors and AUCof: 950 with accuracy of 89.9% using the single best axis foreach activity alone. Adding a gyroscope with the accelerometerincreased the average AUC from: 968 to: 974, increasing theaccuracy of specific movements as much as 2%. Results show that, while a combination of accelerometer and gyroscope provide thestrongest classification results, often times features extracted froma single, best axis are enough to accurately identify movementsfor a personal training routine, where that axis is often, but notalways, an accelerometer axis.

Demo: ExerLink - enabling pervasive social exergames with heterogeneous exercise devices

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.

Near-Realistic Mobile Exergames With Wireless Wearable Sensors

Exergaming is expanding as an option for sedentary behavior in childhood/adult obesity and for extra exercise for gamers. This paper presents the development process for a mobile active sports exergame with near-realistic motions through the usage of body-wearable sensors. The process begins by collecting a dataset specifically targeted to mapping real-world activities directly to the games, then, developing the recognition system in a fashion to produce an enjoyable game. The classification algorithm in this paper has precision and recall of 77% and 77% respectively, compared with 40% and 19% precision and recall on current activity monitoring algorithms intended for general daily living activities. Aside from classification, the user experience must be strong enough to be a successful system for adoption. Indeed, fast and intense activities as well as competitive, multiplayer environments make for a successful, enjoyable exergame. This enjoyment is evaluated through a 30 person user study. Multiple aspects of the exergaming user experience trials have been merged into a comprehensive survey, called ExerSurvey. All but one user thought the motions in the game were realistic and difficult to cheat. Ultimately, a game with near-realistic motions was shown to be an enjoyable, active video exergame for any environment.

Remote patient monitoring: what impact can data analytics have on cost?

While significant effort has been made on designing Remote Monitoring Systems (RMS), limited research has been conducted on the potential cost savings that these systems offer in terms of reduction in readmission costs, as well as the costs associated with human resources involved in the intervention process. This paper is particularly interested in exploring potential cost savings that an analytics engine can provide in presence of intelligent back-end data processing and machine learning algorithms against conventional RMS that operate based on simple thresholding approaches. Using physiological data collected from 486 heart failure patients through a clinical study in collaboration with the UCLA School of Medicine, we conduct a retrospective data analysis to estimate prediction accuracy as well as associated costs of the two remote monitoring approaches. Our results show that analytics-based RMS can reduce false negative rates by 61.4% while maintaining a false positive performance close to that of conventional RMS. Furthermore, the proposed analytics engine achieves 61.5% reduction in the overall readmission costs.

MET calculations from on-body accelerometers for exergaming movements

The use of accelerometers to approximate energy expenditure and serve as inputs for exergaming, have both increased in prevalence in response to the worldwide obesity epidemic. Exergames have a need to show energy expenditure values to validate their results, often using accelerometer approximations applied to general daily-living activities. This work presents a method for estimating the metabolic equivalent of task (MET) values achieved when users perform exergaming-specific movements. This shows the caloric expenditure achieved by active video games, based upon raw gravity values of accelerations. Results show that, while a fusion of sensors monitoring the entire body achieves the best results, sensors placed closest to the primary location of movement achieve the most accurate approximations to the METs achieved per activity as well as the overall MET achieved for the soccer exergame under consideration. The METs achieved approach 7, the value considered to be actual casual soccer game play.

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate.

Multimodal energy expenditure calculation for pervasive health: A data fusion model using wearable sensors

Accurate estimation of energy expenditure during exercise is important for professional athletes and casual users alike, for designing training programs and meeting their fitness goals. However, producing an accurate estimate in a mobile, wearable health-monitoring system is challenging because most calculations require knowledge of the subjects movement speed. Though determining precise movement speed is trivial on a treadmill, inaccuracies of the sensors in a mobile system have a negative impact on the accuracy of the final energy expenditure estimate. In this paper, we propose a novel method to calculate energy expenditure using sensor fusion, in which data from multiple sensors is combined to formulate the result, based on a linear-regression model. We combine data from our wearable system with embedded pulse sensor and pedometer to produce an estimate that is far more accurate than possible with the pedometer alone, reducing our mean-absolute error by 64.3%. These results indicate that it is possible to obtain an accurate energy expenditure estimate in a multi-sensor system, even with affordable, low-cost, and pervasive components that may not be accurate individually.

A Pervasive Assessment of Motor Function: A Lightweight Grip Strength Tracking System

With the growing cost associated with the diagnosis and treatment of chronic neuro-degenerative diseases, the design and development of portable monitoring systems becomes essential. Such portable systems will allow for early diagnosis of motor function ability and provide new insight into the physical characteristics of ailment condition. This paper introduces a highly mobile and inexpensive monitoring system to quantify upper-limb performance for patients with movement disorders. With respect to the data analysis, we first present an approach to quantify general motor performance using the introduced sensing hardware. Next, we propose an ailment-based analysis which employs a significant-feature identification algorithm to perform cross-patient data analysis and classification. The efficacy of the proposed framework is demonstrated using real data collected through a clinical trial. The results show that the system can be utilized as a preliminary diagnostic tool to inspect the level of hand-movement performance. The ailment-based analysis performs an intergroup comparison of physiological signals for cerebral vascular accident (CVA) patients, chronic inflammatory demyelinating polyneuropathy (CIDP) patients, and healthy individuals. The system can classify each patient group with an accuracy of up to 95.00% and 91.42% for CVA and CIDP, respectively.

MARHS: mobility assessment system with remote healthcare functionality for movement disorders

Due to the global trend of aging societies with increasing demand for low cost and high quality healthcare services, there has been extensive research and development directed toward wireless and remote healthcare technology that considers age-associated ailments. In this paper, we introduce Mobility Assessment and Remote Healthcare System (MARHS) that utilizes a force sensor in order to provide quantitative assessment of the mobility level of patients with movement disorder ailment, which is one common age-associated ailment. The proposed system also enables the remote healthcare services that allow patients to receive diagnoses from clinical experts without his/her presence. MARHS also contains a data analysis unit in order to provide information that summarizes the characteristics of symptoms of a group of patients (e.g., patients with a certain type of ailment) using a combination of feature ranking, feature selection, and classification algorithms. The results of the analyses on the data from a clinical trial show that the examination results of the proposed system can accurately recognize various groups of patients, such as, patients with (i) chronic obstructive pulmonary disease, (ii) hypertension, and (iii) cerebral vascular accident with an average accuracy of 90.05%, 82.60%, and 93.54%, respectively.

Utilization of a novel digital measurement tool for quantitative assessment of upper extremity motor dexterity: a controlled pilot study

BackgroundThe current methods of assessing motor function rely primarily on the clinician’s judgment of the patient’s physical examination and the patient’s self-administered surveys. Recently, computerized handgrip tools have been designed as an objective method to quantify upper-extremity motor function. This pilot study explores the use of the MediSens handgrip as a potential clinical tool for objectively assessing the motor function of the hand.MethodsEleven patients with cervical spondylotic myelopathy (CSM) were followed for three months. Eighteen age-matched healthy participants were followed for two months. The neuromotor function and the patient-perceived motor function of these patients were assessed with the MediSens device and the Oswestry Disability Index respectively. The MediSens device utilized a target tracking test to investigate the neuromotor capacity of the participants. The mean absolute error (MAE) between the target curve and the curve tracing achieved by the participants was used as the assessment metric. The patients’ adjusted MediSens MAE scores were then compared to the controls. The CSM patients were further classified as either “functional” or “nonfunctional” in order to validate the system’s responsiveness. Finally, the correlation between the MediSens MAE score and the ODI score was investigated.ResultsThe control participants had lower MediSens MAE scores of 8.09%±1.60%, while the cervical spinal disorder patients had greater MediSens MAE scores of 11.24%±6.29%. Following surgery, the functional CSM patients had an average MediSens MAE score of 7.13%±1.60%, while the nonfunctional CSM patients had an average score of 12.41%±6.32%. The MediSens MAE and the ODI scores showed a statistically significant correlation (r=-0.341, p<1.14×10-5). A Bland-Altman plot was then used to validate the agreement between the two scores. Furthermore, the percentage improvement of the the two scores after receiving the surgical intervention showed a significant correlation (r=-0.723, p<0.04).ConclusionsThe MediSens handgrip device is capable of identifying patients with impaired motor function of the hand. The MediSens handgrip scores correlate with the ODI scores and may serve as an objective alternative for assessing motor function of the hand.