In-Home Assistive System for Traumatic Brain Injury Patients

Ince, Nuri F.; Min, Cheol-Hong; Tewfik, A.H.

doi:10.1109/icassp.2007.366298

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Therefore, the GMM outputs give many local outputs that cause a high false positive recognition rate. In our previous work, we utilized a fixed window majority voter (MV) procedure to remove local errors [15]. The majority voter used 16 points ( ∼ =10 s) windows to decide whether the observation sequence is related to any of the tasks of interest.…”

Section: Postprocessing and Final Decisionmentioning

confidence: 99%

Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors

Ince

Min

Tewfik

et al. 2007

EURASIP J. Adv. Signal Process.

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This paper describes a flexible, cost-effective, wireless in-home activity monitoring system for assisting patients with cognitive impairments due to traumatic brain injury (TBI). The system locates the subject with fixed home sensors and classifies early morning bathroom activities of daily living with a wearable wireless accelerometer. The system extracts time-and frequencydomain features from the accelerometer data and classifies these features with a hybrid classifier that combines Gaussian mixture models and a finite state machine. In particular, the paper establishes that despite similarities between early morning bathroom activities of daily living, it is possible to detect and classify these activities with high accuracy. It also discusses system training and provides data to show that with proper feature selection, accurate detection and classification are possible for any subject with no subject specific training.

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Section: Postprocessing and Final Decisionmentioning

confidence: 99%

Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors

Ince

Min

Tewfik

et al. 2007

EURASIP J. Adv. Signal Process.

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“…Thus they are not suited for home based monitoring. The wearable sensing modalities for TBI are robotic devices for a specific set of exercises or accelerometer based sensing and classifying specific set of motions [9, 10]. We propose an inexpensive, easy to use system requiring minimal setup called MotionTalk, which is capable of full body physical rehabilitation using Microsoft Kinect.…”

Section: Introductionmentioning

confidence: 99%

Kinect-based rehabilitation system for patients with traumatic brain injury

Venugopalan

Cheng

Stokes

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Traumatic brain injury is the leading cause of death and disability in the age group of 0 – 44 years. Though physical exercises have proven benefits in the rehabilitation process, the compliance rate of patients in the home environment is poor. In this paper we propose a system, MotionTalk, which captures and analyses motion data acquired using Microsoft Kinect. It gives a real time quantitative assessment of the exercises performed by TBI patients at home with respect to the same exercises performed in the clinic by utilizing relatively inexpensive contactless sensing and dynamic programming techniques. We compare this system to previous reminder systems, wearable systems, and motion capture systems. MotionTalk is less intrusive in nature and inexpensive to deploy at home because it is based on readily available hardware.

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“…Therefore the GMM outputs give many local outputs that cause a high false positive recognition rate. In our previous work we have utilized a fixed window majority voter procedure to remove local errors [5]. The MV used 16 points Box-1.…”

Section: Post Processing and Final Decisionmentioning

confidence: 99%

“…For this purpose to collect ADL data, we recently developed an in-home data acquisition system which integrates two wireless sensors systems [4,5]. The first sensor system is a collection of fixed wireless home sensors.…”

Section: Introductionmentioning

confidence: 99%

A feature combination approach for the detection of early morning bathroom activities with wireless sensors

Ince

Min

Tewfik

2007

Proceedings of the 1st ACM SIGMOBILE International Workshop on Systems and Networking Support for Healthcare and Assisted Livin

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Abstract-In this paper we investigate the use of wearable accelerometers and wireless home sensors for the detection of early morning daily activities. In particular we focus on the detection of brushing, washing face and shaving activities by using a wireless accelerometer sensor attached to the right wrist of subjects to collect the hand movement data. First the system localizes the subject in the bathroom with a wireless home motion sensor. This is followed by extraction of time and frequency domain features of accelerometer data. These features are input to an ensemble of Gaussian mixture models which represent each activity we focus on and then post processed by a finite state machine for classification. We show promising experimental results from 7 subjects while completing washing face, shaving and brushing activities. The proposed system achieved 93.5%, 92.5% and 95.6% classification accuracy in the recognition of these three tasks respectively.

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In-Home Assistive System for Traumatic Brain Injury Patients

Cited by 3 publications

References 4 publications

Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors

Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors

Kinect-based rehabilitation system for patients with traumatic brain injury

A feature combination approach for the detection of early morning bathroom activities with wireless sensors

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