Sensor-enabled RFID system for monitoring arm activity in daily life

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients' health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants' homes over three months. Given the extensive amount of data, only a portion of the participants' data was used for this specific analysis. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional-de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients.

RFID in Healthcare – Current Trends and the Future

Radio Frequency Identification (RFID) enables automatic identification of objects using radio waves. The identified objects can be in and out of the line of sight and there is no need for physical contact with them. RFID technology is deployed in a wide range of industries such as supply chain management, inventory control, farming (to track animals), e-Passports, the tracking of humans (in prisons and hospitals) and in healthcare [1]. The three key elements of an RFID system are the tags, readers and the backend server. Tags are devices physically attached to objects and readers (wired or mobile) recognize the presence of objects in its range.

Accelerometry measuring the outcome of robot-supported upper limb training in chronic stroke: a randomized controlled trial

PURPOSE

This study aims to assess the extent to which accelerometers can be used to determine the effect of robot-supported task-oriented arm-hand training, relative to task-oriented arm-hand training alone, on the actual amount of arm-hand use of chronic stroke patients in their home situation.

METHODS

This single-blind randomized controlled trial included 16 chronic stroke patients, randomly allocated using blocked randomization (n = 2) to receive task-oriented robot-supported arm-hand training or task-oriented (unsupported) arm-hand training. Training lasted 8 weeks, 4 times/week, 2 × 30 min/day using the (T-)TOAT ((Technology-supported)-Task-Oriented-Arm-Training) method. The actual amount of arm-hand use, was assessed at baseline, after 8 weeks training and 6 months after training cessation. Duration of use and intensity of use of the affected arm-hand during unimanual and bimanual activities were calculated.

RESULTS

Duration and intensity of use of the affected arm-hand did not change significantly during and after training, with or without robot-support (i.e. duration of use of unimanual use of the affected arm-hand: median difference of -0.17% in the robot-group and -0.08% in the control group between baseline and after training cessation; intensity of the affected arm-hand: median difference of 3.95% in the robot-group and 3.32% in the control group between baseline and after training cessation). No significant between-group differences were found.

CONCLUSIONS

Accelerometer data did not show significant changes in actual amount of arm-hand use after task-oriented training, with or without robot-support. Next to the amount of use, discrimination between activities performed and information about quality of use of the affected arm-hand are essential to determine actual arm-hand performance.

TRIAL REGISTRATION

Controlled-trials.com ISRCTN82787126.

Sensor-enabled RFID system for monitoring arm activity: reliability and validity

After stroke, capacity to complete tasks in the treatment setting with the more-affected arm is an unreliable index of actual use of that extremity in daily life. Available objective methods for monitoring real-world arm use rely on placing movement sensors on patients. These methods provide information on amount but not type of arm activity, e.g., functional versus nonfunctional movement. This paper presents an approach that places sensors on patients and household objects, overcoming this limitation. An accelerometer and the transmitter component of a radio-frequency proximity sensor are attached to objects; the receiver component is attached to the arm of interest. The receiver triggers an on-board radio-frequency identification tag to signal proximity when that arm is within 23 cm of an instrumented object. In benchmark testing, this system detected perfectly which arm was used to move the target object on 200 trials. In a laboratory study with 35 undergraduates, increasing the amount of time target objects were moved with the arm of interest resulted in a corresponding increase in system output . Moreover, measurement error was low ( ≤ 2.5%). The results support this system's reliability and validity in individuals with unimpaired movement; testing is now warranted in stroke patients.