Millions of surgeries are performed in the US annually, and numbers are trending upwards. Traditional rehabilitative interventions are struggling to meet current demands, and researchers have turned to pre-operative interventions, or prehabilitation, to improve patient functions. However, existing literature primarily discusses efficacy or the use of commercial sensing devices, and lacks a clear comprehension of healthcare professionals' (HPs') needs and perspectives. User-centered stakeholder understandings are crucial for a technology's adoption, but prehabilitation literature lacks such understandings. Therefore we conduct semi-structured interviews with 12 prehabilitation healthcare professionals (HPs) to offer descriptions of care challenges, tool usage, and perspectives regarding suitable and effective technologies. These data can assist designers in fostering prehabilitation processes via tailored prehabilitation tools which meet HPs' needs and expectations.

Prehabilitation: Care Challenges and Technological Opportunities

Adaptive body movement system for wearable IoT instruments based on matrix vector parameter estimation

Low-Cost Health Monitoring Pedometer Using IoT

We propose a control method for a two-wheeled robot that can carry a load in a staircase environment without increasing the burden on humans through power-assist control using admittance control. Considering that the motion of lifting the robot burdens a human in a staircase environment, it was necessary to reduce the peak value of the human force when pulling the robot upward. We evaluated the proposed, conventional, and the case without control (no control’’) methods by simulations and experiments on actual machines. Hence, the proposed method reduces the human workload to about 75% of that of the conventional method and about 50% of that without control. The proposed method can also enable the machine climb upstairs at a velocity of 1 step/s, which is about three times faster than the conventional method.

Developing power-assisted two-wheeled luggage-carrying robot for stair-lifting using admittance control

AbstractWorld is surrounded with key buzz emerging from technological use by common man. Technology lends a hand to achieve the fitness by providing assistance. Walking is predominantly and usually preferred exercise by mass. Walking pattern analysis or gait analysis expands the probable zone for researcher to contribute in gaining and maintaining fitness for common people. There are number of parameters influencing walking pattern of the person. The motive of this study is to verify the relation among various spatiotemporal parameters used in gait analysis. Paper tests and comments on correlation among selected spatiotemporal parameters on the basis of analysis performed on 50 healthy participants from Pune, India. Contributing participants are in the age group of 20–75 years combining male and female members. GAITRite Walkway is used to perform recording of the gait pattern. Pearson’s correlation coefficient is used for the testing the correlation within two selected parameters. The correlation coefficient varies within range of −1 to +1, indicating positive, negative, or no correlation. Correlation results are verified through application of them in classification of subjects in two age groups. Support vector machine, logistic regression, and K-nearest neighbor machine learning approaches are tested.KeywordsSpatiotemporal parametersCorrelation coefficientMachine learningGait analysis 

Perceiving Correlation Among Spatiotemporal Gait Parameters and Verifying Its Relation Using Machine Learning Classification Technique Pilot Study for Indian Population

Gait analysis that is used to measure human activity level, is an important aspect for the health professionals. It helps in understanding the limitations caused in the locomotion due to some orthopedic conditions or some neurological disorders. Health analysis though of key importance is yet, limited to the routine pathological monitoring and dietitian consultation. A possible Extension of it could be pre--habilitation (Pre-hab) process for wellness. Pre-hab process helps to prepare your body for high level of activities, reduces the possible risk of injury and ensures quick recovery. The work proposed in the paper emphasize on aspect of Recognition of Gait Pattern for pre-hab through the body worn sensors comprising of accelerometer, gyroscope & electromyography. Continuous remote monitoring is done using sensor connectivity to internet of things (IoT). The analysis of normal or abnormal gait is done using the Data Analytics Module.

Pre-Habilitation and Wellness through Gait Analysis using Body Worn Sensors

Walking is the commonly adopted exercise to maintain and regain the fitness. The very basic thing which layman can understand is observed variation in walking speed during different phases, monitoring of such variation at early stage helps out to know the health condition in certain diseases. To understand the influence of Barefoot Walk and with Shoe Walk, Gait Velocity is selected for basic analysis of the gait pattern in this paper. Fifty subjects within age group of 25 to 75 years are selected for data collection and requested to walk barefoot and using comfortable footwear. Using GAITRite mat platform working on pressure sensors logic, data is recorded and results are tested. Results shows that the barefoot velocity remains on lower side compared with shoe walk. While, when compared along with age group it shows linear relation between gait velocity and age. As age increases the velocity decreases.

Verifying the Effect of Barefoot Walk and Comfortable Shoe Walk using Gait Velocity as a Tool for Analysis

With the widespread technological development in the field of Wireless sensor network (WSN) and Internet of Things (IoT), provides various domain to take upon the challenges for new development of various applications. These technologies are utilized by layman without having awareness of, how they can use it for better results? FitnessMantra is the theme put forward in this paper, suggesting the collaborative architecture of Fitness Band, its inbound software, data analytics and Internet of Things. FitnessMantra is the solution set forth considering today's requirement of health monitoring. Paper looks after the issues of monitoring the walking activity (Gait Cycle Pattern) of patients under the process of rehabilitation after major/ critical leg surgeries. Architecture proposes collaborative working of Fitness band, data aggregation through related application, analysis of collected data and communication gateway for future updates. Typically used sensors for monitoring the walk is Accelerometer or pedometer. Further communication and analysis of data varies person by person.

Fitness mantra: Theme for health analysis in rehabilitation process

Obesity is becoming one of the prevalent lifestyle diseases across the globe; need to be deal with behavioral intervention through self-management and motivation in line with rigorous physical exercise. When it is a matter to handle obesity to regain the health, parameters like: self-motivation, self-control, guided treatment, counseling, monitored exercise, and medical assistance are essential in consideration list. Paper proposes the model encompassing three-dimensional care including nutritional intake, counseling, and gait monitoring during exercise. Considering the effect of obesity on biomechanics of foot, gait pattern analysis of obese person provides greater information regarding variations in spatio-temporal parameters. Ever-increasing contribution of behavioral intervention will maintain the line of action in the perfect direction. A selection of accelerometer, gyroscope, and electromyography sensors is appropriate for the cause to derive basic hardware. MSP430 processor and ZigBee module are used for processing information and establishing communication. Within close proximity and placement of nodes at a different level, nodes are able to achieve 90–94% packet delivery ratio in actual environment compare to the 100% packet delivery in simulation environment. Result suggests that with the adaption of accurate classification process, system could be useful for controlled exercise monitoring or for daily activity monitoring, which is working at low-power level with affordable wearable technology in achieving wellness.

Neha Sathe

Papers

Pre-Habilitation and Wellness through Gait Analysis using Body Worn Sensors

Verifying the Effect of Barefoot Walk and Comfortable Shoe Walk using Gait Velocity as a Tool for Analysis

Fitness mantra: Theme for health analysis in rehabilitation process

Achieving Wellness by Monitoring the Gait Pattern with Behavioral Intervention for Lifestyle Diseases

Identifying key gait parameters in gender recognition and classification performance analysis using machine learning algorithms